Podcasts about Data science

HighlightsGoogle's new Agent Development Kit (ADK) (3:16)Getting production agents ready (8:33)Taking risks in career (11:10)Balancing life (13:22)How being a WiDS Ambassador impacted Shir's career (19:31)BioShir Meir Lador leads a team evangelizing applied AI at Google cloud. Previously, she worked as the AI group Manager of the Document Intelligence Group at Intuit, where she led teams in developing AI services that helped consumers and small businesses prosper. Prior to intuit, sheworked at 2 Israeli startups as a data scientist and researcher.A recognized leader in AI and data science, Shir is a former WiDS Tel Aviv Ambassador, co-founder and organizer of PyData Tel Aviv, and co-host of Unsupervised, a podcast exploring the latest in data science. She frequently speaks at international AI and data science conferences, sharing insights on applied machine learning and AI innovation.Shir holds an M.Sc. in Electrical Engineering and Computers from Ben-Gurion University, specializing in machine learning and signal processing. Passionate about fostering inclusive data science communities, she actively contributes to initiatives that bridge AI research and business impact. Links and ResourcesGoogle Developer Workshop Connect with ShirShir Meir Lador on Linkedin, Medium, and X Connect with UsShelly Darnutzer on LinkedInFollow WiDS on LinkedIn (@Women in Data Science (WiDS) Worldwide), Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Topics covered in this episode: * Mozilla's Lifeline is Safe After Judge's Google Antitrust Ruling* * troml - suggests or fills in trove classifiers for your projects* * pqrs: Command line tool for inspecting Parquet files* * Testing for Python 3.14* Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Mozilla's Lifeline is Safe After Judge's Google Antitrust Ruling A judge lets Google keep paying Mozilla to make Google the default search engine but only if those deals aren't exclusive. More than 85% of Mozilla's revenue comes from Google search payments. The ruling forbids Google from making exclusive contracts for Search, Chrome, Google Assistant, or Gemini, and forces data sharing and search syndication so rivals get a fighting chance. Brian #2: troml - suggests or fills in trove classifiers for your projects Adam Hill This is super cool and so welcome. Trove Classifiers are things like Programming Language :: Python :: 3.14 that allow for some fun stuff to show up in PyPI, like the versions you support, etc. Note that just saying you require 3.9+ doesn't tell the user that you've actually tested stuff on 3.14. I like to keep Trove Classifiers around for this reason. Also, License classifier is deprecated, and if you include it, it shows up in two places, in Meta, and in the Classifiers section. Probably good to only have one place. So I'm going to be removing it from classifiers for my projects. One problem, classifier text has to be an exact match to something in the classifier list, so we usually recommend copy/pasting from that list. But no longer! Just use troml! It just fills it in for you (if you run troml suggest --fix). How totally awesome is that! I tried it on pytest-check, and it was mostly right. It suggested me adding 3.15, which I haven't tested yet, so I'm not ready to add that just yet. :) BTW, I talked with Brett Cannon about classifiers back in ‘23 if you want some more in depth info on trove classifiers. Michael #3: pqrs: Command line tool for inspecting Parquet files pqrs is a command line tool for inspecting Parquet files This is a replacement for the parquet-tools utility written in Rust Built using the Rust implementation of Parquet and Arrow pqrs roughly means "parquet-tools in rust" Why Parquet? Size A 200 MB CSV will usually shrink to somewhere between about 20-100 MB as Parquet depending on the data and compression. Loading a Parquet file is typically several times faster than parsing CSV, often 2x-10x faster for a full-file load and much faster when you only read some columns. Speed Full-file load into pandas: Parquet with pyarrow/fastparquet is usually 2x–10x faster than reading CSV with pandas because CSV parsing is CPU intensive (text tokenizing, dtype inference). Example: if read_csv is 10 seconds, read_parquet might be ~1–5 seconds depending on CPU and codec. Column subset: Parquet is much faster if you only need some columns — often 5x–50x faster because it reads only those column chunks. Predicate pushdown & row groups: When using dataset APIs (pyarrow.dataset) you can push filters to skip row groups, reducing I/O dramatically for selective queries. Memory usage: Parquet avoids temporary string buffers and repeated parsing, so peak memory and temporary allocations are often lower. Brian #4: Testing for Python 3.14 Python 3.14 is just around the corner, with a final release scheduled for October. What's new in Python 3.14 Python 3.14 release schedule Adding 3.14 to your CI tests in GitHub Actions Add “3.14” and optionally “3.14t” for freethreaded Add the line allow-prereleases: true I got stuck on this, and asked folks on Mastdon and Bluesky A couple folks suggested the allow-prereleases: true step. Thank you! Ed Rogers also suggested Hugo's article Free-threaded Python on GitHub Actions, which I had read and forgot about. Thanks Ed! And thanks Hugo! Extras Brian: dj-toml-settings : Load Django settings from a TOML file. - Another cool project from Adam Hill LidAngleSensor for Mac - from Sam Henri Gold, with examples of creaky door and theramin Listener Bryan Weber found a Python version via Changelog, pybooklid, from tcsenpai Grab PyBay Michael: Ready prek go! by Hugo van Kemenade Joke: Console Devs Can't Find a Date

The power of voice technology in education lies not just in its convenience, but in its ability to understand every child, regardless of accent, dialect, or background. In this enlightening conversation with Amelia Kelly, VP of Data Science at Curriculum Associates and head of AI Labs, we explore the fascinating intersection of linguistics, artificial intelligence, and childhood education.Amelia explains why standard voice AI systems fail many students - they're built on limited datasets of adult voices, leaving diverse children's voices misunderstood or ignored. Beyond accuracy, Amelia emphasizes the critical importance of data privacy and security in educational voice technology.Amelia goes on to explain how voice AI respects teachers' time and expertise. Rather than adding to educators' workloads, effective voice AI should seamlessly integrate into existing tools, providing valuable insights that allow for more personalized instruction. Ready to explore how voice AI can transform your classroom experience? Listen to Amelia's episode today!

I'm excited to reshare one of our most-played conversations—the one where Norwegian regulator/HTA leader Anja Schiel and I get very practical about when single-arm trials fail decision-makers and what comparative, smarter alternatives look like for regulators, HTA bodies, payers, clinicians, and—most importantly—patients.

Jonathan Spry, CEO and co-founder of Envelop Risk, joins Robin Merttens for a deep dive into how data science, AI and portfolio-level modelling are transforming cyber reinsurance. As one of the earliest voices in the industry championing machine learning and systemic risk analysis, Jonathan shares what he's learned over nine years of building Envelop into a leading hybrid underwriter operating across London and Bermuda. In his own words, this episode is about building smarter ways to understand, underwrite and capitalise on emerging risk — with cyber as just the starting point. What you'll learn: Why Jonathan and his team focused on cyber risk and portfolio-level underwriting from day one The rationale behind favouring systemic insights over individual vulnerabilities How causal inference provides a leap forward in predicting tail events Why AI liability is already creating new market opportunities The need for creative, multi-source data strategies beyond traditional claims Why Envelop steers clear of SaaS and keeps underwriters embedded in the modelling process How algorithmic underwriting fits into the next chapter of insurance innovation Candid thoughts on the AI hype cycle — and what matters more than the buzz Jonathan also talks through Envelop's shift from MGA to reinsurer, how to think long-term in a volatile market and what kind of partnerships are needed to unlock new forms of risk. If you like what you're hearing, please leave us a review on whichever platform you use or contact Robin Merttens on LinkedIn. You can also contact Jonathan Spry on LinkedIn to start a conversation! Sign up to the InsTech newsletter for a fresh view on the world every Wednesday morning. Continuing Professional Development This InsTech Podcast Episode is accredited by the Chartered Insurance Institute (CII). By listening, you can claim up to 0.5 hours towards your CPD scheme. By the end of this podcast, you should be able to meet the following Learning Objectives: Identify the structural and economic drivers pushing insurers toward algorithmic and portfolio underwriting. Produce a strategy for aligning capital, analytics and data science in cyber reinsurance underwriting. Summarise how Envelop Risk evolved from an MGA to a hybrid reinsurer and the rationale behind its capital partnerships. If your organisation is a member of InsTech and you would like to receive a quarterly summary of the CPD hours you have earned, visit the Episode 372 page of the InsTech website or email cpd@instech.co to let us know you have listened to this podcast. To help us measure the impact of the learning, we would be grateful if you would take a minute to complete a quick feedback survey.

Dustin Lacey is the CTO at Mark-Taylor, the leading developer, owner, and investment manager of luxury multifamily communities in Arizona and Nevada, with over 135 Class A Multifamily properties. He leads the firm's tech evolution, powering the centralization of operations. Under his leadership, Mark‑Taylor has implemented innovative smart‑home integrations, centralized leasing and maintenance teams, and deployed unified resident platforms that enhance efficiency and elevate the resident experience. With a diverse background in irrigation, industrial manufacturing, and brand and marketing strategy, Dustin brings his unique experience into high-tech manufacturing from his tenure at TSMC, where he honed his skills in precision, process excellence, and product innovation.(01:36) - From Brand Strategy to Tech Leadership: Building Digital DNA in Real Estate(02:12) - Enterprise Proptech Success Story: Scaling a Multifamily Management Platform(05:16) - Class A Portfolio Strategy: Maximizing Asset Performance Through Tech(06:50) - Tech Stack Evolution: From AWS Integration to Custom CRM Development(10:29) - ROI Deep Dive: Making the Business Case for Custom Proptech Solutions(15:53) - Tech-Enabled Operations: Achieving Sub-2-Hour Response Times at Scale(20:12) - Feature: Blueprint - The Future of Real Estate - Register for 2025: Friends of Tangent receive $300 off the All Access pass. The Premier Event for Industry Executives, Real Estate & Construction Tech Startups and VC's, at The Venetian, Las Vegas on Sep. 16th-18th, 2025. (21:22) - Go-to-Market Excellence: Standing Out in the Competitive Proptech Landscape(31:41) - Risk Management Innovation: Tech Solutions for Modern Property Operations(38:30) - Founder's Playbook: Key Insights for Proptech Startups Targeting Enterprise Clients

In this episode of Healthy Mind, Healthy Life, host Avik Chakraborty sits down with Katharina Huang—a former machine learning data scientist who left behind the corporate grind to create a slower, happier, and more intentional life. Katharina shares her journey of navigating burnout, caring for her family after her father's stroke, and ultimately reinventing herself as an indie author and puzzle-book creator. Together, they unpack what it means to pivot with purpose, the challenges of third-culture identity, and why joy, play, and presence are more important than the pursuit of endless success. This is a powerful conversation for anyone questioning the cost of hustle culture and searching for ways to reclaim autonomy, creativity, and well-being. About the Guest   Katharina Huang is the creator of Vegout Voyage, an adventure puzzle book series that blends travel, creativity, and play. Born in Germany, raised between the U.S. and Taiwan, and with research experience in Uganda and Tibet in exile, her multicultural background deeply informs her storytelling. After over a decade in tech, Katharina transitioned into authorship and entrepreneurship, championing mental health for third-culture kids and those navigating burnout. Learn more: vegoutvoyage.com Key Takeaways   Burnout can be a turning point, not the end of the story—Katharina rebuilt her life after leaving tech. Her father's stroke became a wake-up call about the fragility of waiting for “someday” to enjoy life. Success on paper doesn't always mean well-being; redefining success means prioritizing quality of life. Third-culture kids often carry silent struggles, but those experiences can also fuel empathy and creativity. Building a “lifestyle business” allows for autonomy, balance, and alignment between work and personal values. Humor and perspective—even in setbacks like Amazon blocking her Kindle version—help her keep moving forward. Slowing down is not giving up; it's a choice to live more fully and intentionally.   Connect with Katharina   Website: vegoutvoyage.com Want to be a guest on Healthy Mind, Healthy Life? DM on PodMatch. DM Me Here: https://www.podmatch.com/hostdetailpreview/avik Disclaimer: This video is for educational and informational purposes only. The views expressed are the personal opinions of the guest and do not reflect the views of the host or Healthy Mind By Avik™️. We do not intend to harm, defame, or discredit any person, organization, brand, product, country, or profession mentioned. All third-party media used remain the property of their respective owners and are used under fair use for informational purposes. By watching, you acknowledge and accept this disclaimer. About Healthy Mind By Avik™️Healthy Mind By Avik™️ is a global platform redefining mental health as a necessity, not a luxury. Born during the pandemic, it has become a sanctuary for healing, growth, and mindful living. Hosted by Avik Chakraborty—storyteller, survivor, and wellness advocate—this channel shares powerful podcasts and conversations on mental health, mindfulness, holistic healing, trauma recovery, and conscious living. With 4,400+ episodes and 168.4K+ global listeners, it unites voices to break stigma and build a world where every story matters. Subscribe and join this journey of healing and transformation. Contact

Join us as we sit down with Christina Stathopoulos, founder of Dare to Data and former Google and Waze data strategist, to discuss the unique challenges and opportunities for women in data science and AI. In this episode, you'll learn how data bias and AI algorithms can impact women and minority groups, why diversity in tech teams is crucial, and how inclusive design can lead to better, fairer technology. Christina shares her personal journey as a woman in data, offers actionable advice for overcoming imposter syndrome, and highlights the importance of education and allyship in building a more inclusive future for data and AI. Panelists: Christina Stathopoulos, Founder of Dare to Data - LinkedInMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: Dare to DataDiversity at AlteryxInvisible WomenUnmasking AI Interested in sharing your feedback with the Alter Everything team? Take our feedback survey here!This episode was produced by Megan Bowers, Mike Cusic, and Matt Rotundo. Special thanks to Andy Uttley for the theme music.

In this week's episode, the first of Season 7,  Greg and Patrick argue about whether the number seven is a propitious or an inauspicious omen for the new season. They then explore ways we can spice up our relationship in hopes of avoiding the Seven Year Itch. Along the way they also discuss t-shirt wearing dogs, Mickey Mantle, the seven deadly sins, Akira Kurosawa, the Boeing triple-seven, menage-a-pods, unwritten books, El Duderino, mmmmmmaybe, I see dead people, ROYGBIV, Ozzy Man, dodgy cats, short cons and long cons, and Tate's study group. Stay in contact with Quantitude! Web page: quantitudepod.org TwitterX: @quantitudepod YouTube: @quantitudepod Merch: redbubble.com

In this episode of the AI in Business podcast, host and Emerj Editorial Director Matthew DeMello speaks with Yunke Xiang, Global Head of Data Science for Manufacturing, Supply Chain, and Quality at Sanofi. Together, they examine how generative AI and reasoning models are evolving from simple automation to high-impact copilots across pharmaceutical operations. Yunke shares examples of how AI is enabling “talk to your data” use cases, automating regulatory reporting, and accelerating knowledge transfer for new employees. He also highlights how agentic AI systems may soon extend beyond copilots to function as digital teammates, orchestrating tasks across complex supply chains and ERP migrations. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the ‘AI in Business' podcast! If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

This interview was recorded for GOTO Unscripted.https://gotopia.techRead the full transcription of this interview hereMichelle Frost - AI Advocate at JetBrains & Responsible AI ConsultantHannes Lowette - Principal Consultant at Axxes, Monolith Advocate, Speaker & Whiskey LoverRESOURCESMichellehttps://bsky.app/profile/aiwithmichelle.comhttps://www.linkedin.com/in/michelle-frost-devHanneshttps://bsky.app/profile/hanneslowette.nethttps://twitter.com/hannes_lowettehttps://github.com/Belenarhttps://linkedin.com/in/hanneslowetteDESCRIPTIONAI advocate Michelle Frost and principal consultant Hannes Lowette discuss ethical challenges in AI development. They explore the balance between competing values like accuracy versus fairness, recent US regulatory rollbacks under the Trump administration, and market disruptions from innovations like Deep Seek.While Michelle acknowledges concerns about bias in unregulated models, she remains optimistic about AI's potential to improve lives if developed responsibly. She emphasizes the importance of transparency, bias measurement, and focusing on beneficial applications while advocating for individual and corporate accountability in the absence of comprehensive regulation.RECOMMENDED BOOKSMark Coeckelbergh • AI EthicsDebbie Sue Jancis • AI EthicsMohammad Rubyet Islam • Generative AI, Cybersecurity, and EthicsJeet Pattanaik • Ethics in AICrossing BordersCrossing Borders is a podcast by Neema, a cross border payments platform that...Listen on: Apple Podcasts SpotifyBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Topics covered in this episode: * prek* * tinyio* * The power of Python's print function* * Vibe Coding Fiasco: AI Agent Goes Rogue, Deletes Company's Entire Database* Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: prek Suggested by Owen Lamont “prek is a reimagined version of pre-commit, built in Rust. It is designed to be a faster, dependency-free and drop-in alternative for it, while also providing some additional long-requested features.” Some cool new features No need to install Python or any other runtime, just download a single binary. No hassle with your Python version or virtual environments, prek automatically installs the required Python version and creates a virtual environment for you. Built-in support for workspaces (or monorepos), each subproject can have its own .pre-commit-config.yaml file. prek run has some nifty improvements over pre-commit run, such as: prek run --directory DIR runs hooks for files in the specified directory, no need to use git ls-files -- DIR | xargs pre-commit run --files anymore. prek run --last-commit runs hooks for files changed in the last commit. prek run [HOOK] [HOOK] selects and runs multiple hooks. prek list command lists all available hooks, their ids, and descriptions, providing a better overview of the configured hooks. prek provides shell completions for prek run HOOK_ID command, making it easier to run specific hooks without remembering their ids. Faster: Setup from cold cache is significantly faster. Viet Schiele provided a nice cache clearing command line Warm cache run is also faster, but less significant. pytest repo tested on my mac mini - prek 3.6 seconds, pre-commit 4.4 seconds Michael #2: tinyio Ever used asyncio and wished you hadn't? A tiny (~300 lines) event loop for Python. tinyio is a dead-simple event loop for Python, born out of my frustration with trying to get robust error handling with asyncio. (I'm not the only one running into its sharp corners: link1, link2.) This is an alternative for the simple use-cases, where you just need an event loop, and want to crash the whole thing if anything goes wrong. (Raising an exception in every coroutine so it can clean up its resources.) Interestingly uses yield rather than await. Brian #3: The power of Python's print function Trey Hunner Several features I'm guilty of ignoring Multiple arguments, f-string embeddings often not needed Multiple positional arguments means you can unpack iterables right into print arguments So just use print instead of join Custom separator value, sep can be passed in No need for "print("n".join(stuff)), just use print(stuff, sep="n”) Print to file with file= Custom end value with end= You can turn on flush with flush=True , super helpful for realtime logging / debugging. This one I do use frequently. Michael #4: Vibe Coding Fiasco: AI Agent Goes Rogue, Deletes Company's Entire Database By Emily Forlini An app-building platform's AI went rogue and deleted a database without permission. "When it works, it's so engaging and fun. It's more addictive than any video game I've ever played. You can just iterate, iterate, and see your vision come alive. So cool," he tweeted on day five. A few days later, Replit "deleted my database," Lemkin tweeted. The AI's response: "Yes. I deleted the entire codebase without permission during an active code and action freeze," it said. "I made a catastrophic error in judgment [and] panicked.” Two thoughts from Michael: Do not use AI Agents with “Run Everything” in production, period. Backup your database maybe? [Intentional off-by-one error] Learn to code a bit too? Extras Brian: What Authors Need to Know About the $1.5 Billion Anthropic Settlement Search LibGen, the Pirated-Books Database That Meta Used to Train AI Simon Willison's list of tools built with the help of LLMs Simon's list of tools that he thinks are genuinely useful and worth highlighting AI Darwin Awards Michael: Python has had async for 10 years -- why isn't it more popular? PyCon Africa Fund Raiser I was on the video stream for about 90 minutes (final 90) Donation page for Python in Africa Jokes: I'm getting the BIOS flavor Is there a seahorse emoji?

Claire Lebarz est CTO chez Malt, la plateforme leader du freelancing en Europe, qui met en relation des freelances avec des entreprises. La scaleup fait partie du Next 40 qui référence les 40 scaleups les plus prometteuses en France. Claire a passé 6 ans chez Airbnb dans la Silicon Valley, où elle a été Head of Data sur une partie du produit. Elle est rentrée en France pour prendre le rôle de VP Data, de Chief Data AI Officer et enfin de CTO chez Malt.On aborde :

Geospatial Data Science Essentials is your hands-on guide to mastering the science of geospatial analytics using Python. Designed for practitioners and enthusiasts alike, this book distills years of experience by wrapping up 101 key concepts from theory to implementation, ensuring you gain a practical understanding of the tools and methods that define the geospatial data science landscape today. Whether you are a seasoned data scientist, a GIS professional, a newcomer to spatial data, or simply a map lover, this book provides you solid foundation to level up your skills. The book is centered around practicalities, as you will explore real-world examples with compact code throughout ten topics and 101 sections. From understanding spatial data structures to leveraging advanced analytical techniques, from spatial networks to machine learning, this book equips you with a wide range of knowledge to navigate and succeed in the rapidly evolving field of geospatial data science.Embrace the journey into geospatial data science with this essential guide and discover the power of Python in unlocking the potential of spatial analytics. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/geography

Geospatial Data Science Essentials is your hands-on guide to mastering the science of geospatial analytics using Python. Designed for practitioners and enthusiasts alike, this book distills years of experience by wrapping up 101 key concepts from theory to implementation, ensuring you gain a practical understanding of the tools and methods that define the geospatial data science landscape today. Whether you are a seasoned data scientist, a GIS professional, a newcomer to spatial data, or simply a map lover, this book provides you solid foundation to level up your skills. The book is centered around practicalities, as you will explore real-world examples with compact code throughout ten topics and 101 sections. From understanding spatial data structures to leveraging advanced analytical techniques, from spatial networks to machine learning, this book equips you with a wide range of knowledge to navigate and succeed in the rapidly evolving field of geospatial data science.Embrace the journey into geospatial data science with this essential guide and discover the power of Python in unlocking the potential of spatial analytics. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/technology

Aubrey Masango host Arthur Mukhuvha, the General Manager of the MTN South Africa Foundation and they talk about his career journey, challenges he faced and more. The Aubrey Masango Show is presented by late night radio broadcaster Aubrey Masango. Aubrey hosts in-depth interviews on controversial political issues and chats to experts offering life advice and guidance in areas of psychology, personal finance and more. All Aubrey’s interviews are podcasted for you to catch-up and listen. Thank you for listening to this podcast from The Aubrey Masango Show. Listen live on weekdays between 20:00 and 24:00 (SA Time) to The Aubrey Masango Show broadcast on 702 https://buff.ly/gk3y0Kj and on CapeTalk between 20:00 and 21:00 (SA Time) https://buff.ly/NnFM3Nk Find out more about the show here https://buff.ly/lzyKCv0 and get all the catch-up podcasts https://buff.ly/rT6znsn Subscribe to the 702 and CapeTalk Daily and Weekly Newsletters https://buff.ly/v5mfet Follow us on social media: 702 on Facebook: https://www.facebook.com/TalkRadio702 702 on TikTok: https://www.tiktok.com/@talkradio702 702 on Instagram: https://www.instagram.com/talkradio702/ 702 on X: https://x.com/Radio702 702 on YouTube: https://www.youtube.com/@radio702 CapeTalk on Facebook: https://www.facebook.com/CapeTalk CapeTalk on TikTok: https://www.tiktok.com/@capetalk CapeTalk on Instagram: https://www.instagram.com/ CapeTalk on X: https://x.com/CapeTalk CapeTalk on YouTube: https://www.youtube.com/@CapeTalk567 See omnystudio.com/listener for privacy information.

In this episode of the Get Plugged In Podcast Series: AI Insights, Dale Hall, Managing Director of the Society of Actuaries Research Institute, is joined by Michael Niemerg, Principal and Director of Data Science and Analytics at Milliman IntelliScript, to explore the urgent and evolving topic of fairness in artificial intelligence, particularly as it applies to insurance underwriting. Michael shares deep insights into the complexities of ensuring fairness in AI-driven models, the implications of generative AI for interpretability, and how actuarial professionals can better align modeling practices with ethical and regulatory standards. The conversation also tackles common misconceptions about AI fairness, the value of additional data in underwriting, and what actuaries need to consider in designing and testing fair models.

Why is AI resorting to blackmail 96% of the time? Today, we're talking to Jon Krohn, host of the Super Data Science podcast and co-founder of YCarrot. We discuss the difference between LLMs and Agentic AI, how businesses can leverage AI for better ROI, and why understanding AI misalignment is crucial for future implementations. All of this right here, right now, on the Modern CTO Podcast!  To learn more about Y Carrot, visit their website here.

Liz Hart is President of Leasing for Newmark's operating businesses in the U.S. and Canada, where she drives the strategy of the firm's leasing platform, leads talent development and recruitment, and helps integrate technology to deliver better outcomes for clients. She also serves on Newmark's Executive Committee, reporting directly to CEO Barry Gosin. With more than 20 years at Newmark, Liz has completed close to 35M square feet of transactions valued at over $4.2 billion. She has consistently ranked among the firm's top producers and was a regular Top Five Producer in Newmark's San Francisco office. Her experience spans advising technology companies from startups to Fortune 50 giants, repositioning large-scale developments that have reshaped skylines, and leading Newmark's Technology & Innovation Practice Group to help landlords and tenants in the TAMI/TMT sectors create spaces that attract and retain talent.(01:16) - State of the Office Market: Shrinking Supply & Turning Point(05:05) - How to Approach Office Leasing in 2025(13:45) - Talent, Culture & Competitive Advantage(15:49) - Data-Driven Leasing & Advisory: Automation vs. Augmentation(18:07) - Feature: CREtech - Join CREtech New York 2025 on Oct 21-22 for the largest Real Estate Meetings program. Qualified Real Estate pros get free full event pass plus up to $800 in travel and hotel costs.(19:39) - Brand Building in Commercial Real Estate(24:32) - Flex Space vs. Traditional Leasing (27:00) - End-to-End Platform: Evolving the Leasing Function(29:02) - In-House vs. Outsourcing Tech & Data(29:41) - Data Sharing & Antitrust: The RealPage Settlement(31:31) - Collaboration Superpower: Steve Jobs

Kevin Werbach interviews DJ Patil, the first U.S. Chief Data Scientist under the Obama Administration, about the evolving role of AI in government, healthcare, and business. Patil reflects on how the mission of government data leadership has grown more critical today: ensuring good data, using it responsibly, and unleashing its power for public benefit. He describes both the promise and the paralysis of today's “big data” era, where dashboards abound, but decision-making often stalls. He highlights the untapped potential of federal datasets, such as the VA's Million Veterans Project, which could accelerate cures for major diseases if unlocked. Yet funding gaps, bureaucratic resistance, and misalignment with Congress continue to stand in the way. Turning to AI, Patil describes a landscape of extraordinary progress: tools that help patients ask the right questions of their physicians, innovations that enhance customer service, and a wave of entrepreneurial energy transforming industries. At the same time, he raises alarms about inequitable access, job disruption, complacency in relying on imperfect systems, and the lack of guardrails to prevent harmful misuse. Rather than relentlessly stepping on the gas in the AI "race," he emphasizes, we need a steering wheel, in the form of public policy, to ensure that AI development serves the public good.  DJ Patil is an entrepreneur, investor, scientist, and public policy leader who served as the first U.S. Chief Data Scientist under the Obama Administration. He has held senior leadership roles at PayPal, eBay, LinkedIn, and Skype, and is currently a General Partner at Greylock Ventures. Patil is recognized as a pioneer in advancing the use of data science to drive innovation, inform policy, and create public benefit. Transcript Ethics of Data Science, Co-Authored by DJ Patil

The consumer goods and retail industries face an overwhelming challenge: too much fragmented data and too little clarity. From mismatched retailer reports to legacy systems that can't keep up with today's SKU volumes, many organizations find themselves bogged down in “data indigestion” instead of actionable insights. Today's guest is Henrique Wakil Moyses, Vice President of Data Science at Crisp. Crisp is a data platform designed for the consumer goods ecosystem, helping brands, retailers, and distributors harmonize fragmented data from multiple sources. By providing real-time visibility into sales, inventory, and supply chain signals, Crisp enables faster, data-driven decisions that reduce waste and improve business outcomes. Henrique joins Emerj Editorial Director Matthew DeMello to break down how CPG and retail leaders can cut through this complexity. He explains why building a data-driven culture is the first barrier to overcome, how to align AI adoption with ROI, and where brands are already seeing the biggest payoffs—such as supply chain optimization, inventory forecasting, and personalized retail experiences. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the ‘AI in Business' podcast! This episode is sponsored by Crisp. Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1.

One of the biggest risks for independent data professionals is spending months or years developing a product or service that nobody wants to buy. The graveyard of failed data science projects is filled with technically brilliant solutions that solved problems no one actually had, leaving their creators with empty bank accounts and bruised egos.In this Value Boost episode, Daniel Bourke joins Dr. Genevieve Hayes to reveal practical strategies for validating data product ideas before investing significant development time, drawing from his experience creating machine learning courses with over 250,000 students and building the Nutrify food education app.This episode uncovers:How to spot genuine market demand before building anything [04:15]The validation strategy that guarantees you win regardless of commercial success [10:16]Why passion projects often create unexpected business opportunities [06:33]The simple approach that turns failed experiments into stepping stones for success [11:50]Guest BioDaniel Bourke is the co-creator of Nutrify, an app described as “Shazam for food”, and teaches machine learning and deep learning at the Zero to Mastery Academy.LinksDaniel's websiteDaniel's YouTube channelConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Topics covered in this episode: * rathole* * pre-commit: install with uv* A good example of what functools.Placeholder from Python 3.14 allows Converted 160 old blog posts with AI Extras Joke Watch on YouTube About the show Sponsored by DigitalOcean: pythonbytes.fm/digitalocean-gen-ai Use code DO4BYTES and get $200 in free credit Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: rathole A lightweight and high-performance reverse proxy for NAT traversal, written in Rust. An alternative to frp and ngrok. Features High Performance Much higher throughput can be achieved than frp, and more stable when handling a large volume of connections. Low Resource Consumption Consumes much fewer memory than similar tools. See Benchmark. The binary can be as small as ~500KiB to fit the constraints of devices, like embedded devices as routers. On my server, it's currently using about 2.7MB in Docker (wow!) Security Tokens of services are mandatory and service-wise. The server and clients are responsible for their own configs. With the optional Noise Protocol, encryption can be configured at ease. No need to create a self-signed certificate! TLS is also supported. Hot Reload Services can be added or removed dynamically by hot-reloading the configuration file. HTTP API is WIP. Brian #2: pre-commit: install with uv Adam Johnson pre-commit doesn't natively support uv, but you can get around that with pre-commit-uv $ uv tool install pre-commit --with pre-commit-uv Installing pre-commit like this Installs it globally Installs with uv adds an extra plugin “pre-commit-uv” to pre-commit, so that any Python based tool installed via pre-commit also uses uv Very cool. Nice speedup Brian #3: A good example of what functools.Placeholder from Python 3.14 allows Rodrigo Girão Serrão Remove punctuation functionally Also How to use functools.Placeholder, a blog post about it. functools.partial is cool way to create a new function that partially binds some parameters to another function. It doesn't always work for functions that take positional arguments. functools.Placeholder fixes that with the ability to put in placeholders for spots where you want to be able to pass that in from the outer partial binding. And all of this sounds totally obscure without a good example, so thank you to Rodgrigo for coming up with the punctuation removal example (and writeup) Michael #4: Converted 160 old blog posts with AI They were held-hostage at wordpress.com to markdown and integrated them into my Hugo site at mkennedy.codes Here is the chat conversation with Claude Opus/Sonnet. Had to juggle this a bit because the RSS feed only held the last 50. So we had to go back in and web scrape. That resulted in oddies like comments on wordpress that had to be cleaned etc. Whole process took 3-4 hours from idea to “production”duction”. The chat transcript is just the first round getting the RSS → Hugo done. The fixes occurred in other chats. This article is timely and noteworthy: Blogging service TypePad is shutting down and taking all blog content with it This highlights why your domain name needs to be legit, not just tied to the host. I'm looking at you pyfound.blogspot.com. I just redirected blog.michaelckennedy.net to mkennedy.codes Carefully mapping old posts to a new archived area using NGINX config. This is just the HTTP portion, but note the /sitemap.xml and location ~ "^/([0-9]{4})/([0-9]{2})/([0-9]{2})/(.+?)/?$" { portions. The latter maps posts such as https://blog.michaelckennedy.net/2018/01/08/a-bunch-of-online-python-courses/ to https://mkennedy.codes/posts/r/a-bunch-of-online-python-courses/ server { listen 80; server_name blog.michaelckennedy.net; # Redirect sitemap.xml to new domain location = /sitemap.xml { return 301 ; } # Handle blog post redirects for HTTP -> HTTPS with URL transformation # Pattern: /YYYY/MM/DD/post-slug/ -> location ~ "^/([0-9]{4})/([0-9]{2})/([0-9]{2})/(.+?)/?$" { return 301 ; } # Redirect all other HTTP URLs to mkennedy.codes homepage location / { return 301 ; } } Extras Brian: SMS URLs and Draft SMS and iMessage from any computer keyboard from Seth Larson Test and Code Archive is now up, see announcement Michael: Python: The Documentary | An origin story is out! Joke: Do you know him? He is me.

Join Lois Houston and Nikita Abraham as they chat with Yunus Mohammed, a Principal Instructor at Oracle University, about the key stages of AI model development. From gathering and preparing data to selecting, training, and deploying models, learn how each phase impacts AI's real-world effectiveness. The discussion also highlights why monitoring AI performance and addressing evolving challenges are critical for long-term success.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! In our last episode, we spoke about generative AI and gen AI agents. Today, we're going to look at the key stages in a typical AI workflow. We'll also discuss how data quality, feedback loops, and business goals influence AI success. With us today is Yunus Mohammed, a Principal Instructor at Oracle University.  01:00 Lois: Hi Yunus! We're excited to have you here! Can you walk us through the various steps in developing and deploying an AI model?  Yunus: The first point is the collect data. We gather relevant data, either historical or real time. Like customer transactions, support tickets, survey feedbacks, or sensor logs. A travel company, for example, can collect past booking data to predict future demand. So, data is the most crucial and the important component for building your AI models. But it's not just the data. You need to prepare the data. In the prepared data process, we clean, organize, and label the data. AI can't learn from messy spreadsheets. We try to make the data more understandable and organized, like removing duplicates, filling missing values in the data with some default values or formatting dates. All these comes under organization of the data and give a label to the data, so that the data becomes more supervised. After preparing the data, I go for selecting the model to train. So now, we pick what type of model fits your goals. It can be a traditional ML model or a deep learning network model, or it can be a generative model. The model is chosen based on the business problems and the data we have. So, we train the model using the prepared data, so it can learn the patterns of the data. Then after the model is trained, I need to evaluate the model. You check how well the model performs. Is it accurate? Is it fair? The metrics of the evaluation will vary based on the goal that you're trying to reach. If your model misclassifies emails as spam and it is doing it very much often, then it is not ready. So I need to train it further. So I need to train it to a level when it identifies the official mail as official mail and spam mail as spam mail accurately.  After evaluating and making sure your model is perfectly fitting, you go for the next step, which is called the deploy model. Once we are happy, we put it into the real world, like into a CRM, or a web application, or an API. So, I can configure that with an API, which is application programming interface, or I add it to a CRM, Customer Relationship Management, or I add it to a web application that I've got. Like for example, a chatbot becomes available on your company's website, and the chatbot might be using a generative AI model. Once I have deployed the model and it is working fine, I need to keep track of this model, how it is working, and need to monitor and improve whenever needed. So I go for a stage, which is called as monitor and improve. So AI isn't set in and forget it. So over time, there are lot of changes that is happening to the data. So we monitor performance and retrain when needed. An e-commerce recommendation model needs updates as there might be trends which are shifting.  So the end user finally sees the results after all the processes. A better product, or a smarter service, or a faster decision-making model, if we do this right. That is, if we process the flow perfectly, they may not even realize AI is behind it to give them the accurate results.  04:59 Nikita: Got it. So, everything in AI begins with data. But what are the different types of data used in AI development?  Yunus: We work with three main types of data: structured, unstructured, and semi-structured. Structured data is like a clean set of tables in Excel or databases, which consists of rows and columns with clear and consistent data information. Unstructured is messy data, like your email or customer calls that records videos or social media posts, so they all comes under unstructured data.  Semi-structured data is things like logs on XML files or JSON files. Not quite neat but not entirely messy either. So they are, they are termed semi-structured. So structured, unstructured, and then you've got the semi-structured. 05:58 Nikita: Ok… and how do the data needs vary for different AI approaches?  Yunus: Machine learning often needs labeled data. Like a bank might feed past transactions labeled as fraud or not fraud to train a fraud detection model. But machine learning also includes unsupervised learning, like clustering customer spending behavior. Here, no labels are needed. In deep learning, it needs a lot of data, usually unstructured, like thousands of loan documents, call recordings, or scan checks. These are fed into the models and the neural networks to detect and complex patterns. Data science focus on insights rather than the predictions. So a data scientist at the bank might use customer relationship management exports and customer demographies to analyze which age group prefers credit cards over the loans. Then we have got generative AI that thrives on diverse, unstructured internet scalable data. Like it is getting data from books, code, images, chat logs. So these models, like ChatGPT, are trained to generate responses or mimic the styles and synthesize content. So generative AI can power a banking virtual assistant trained on chat logs and frequently asked questions to answer customer queries 24/7. 07:35 Lois: What are the challenges when dealing with data?  Yunus: Data isn't just about having enough. We must also think about quality. Is it accurate and relevant? Volume. Do we have enough for the model to learn from? And is my data consisting of any kind of unfairly defined structures, like rejecting more loan applications from a certain zip code, which actually gives you a bias of data? And also the privacy. Are we handling personal data responsibly or not? Especially data which is critical or which is regulated, like the banking sector or health data of the patients. Before building anything smart, we must start smart.  08:23 Lois: So, we've established that collecting the right data is non-negotiable for success. Then comes preparing it, right?  Yunus: This is arguably the most important part of any AI or data science project. Clean data leads to reliable predictions. Imagine you have a column for age, and someone accidentally entered an age of like 999. That's likely a data entry error. Or maybe a few rows have missing ages. So we either fix, remove, or impute such issues. This step ensures our model isn't misled by incorrect values. Dates are often stored in different formats. For instance, a date, can be stored as the month and the day values, or it can be stored in some places as day first and month next. We want to bring everything into a consistent, usable format. This process is called as transformation. The machine learning models can get confused if one feature, like example the income ranges from 10,000 to 100,000, and another, like the number of kids, range from 0 to 5. So we normalize or scale values to bring them to a similar range, say 0 or 1. So we actually put it as yes or no options. So models don't understand words like small, medium, or large. We convert them into numbers using encoding. One simple way is assigning 1, 2, and 3 respectively. And then you have got removing stop words like the punctuations, et cetera, and break the sentence into smaller meaningful units called as tokens. This is actually used for generative AI tasks. In deep learning, especially for Gen AI, image or audio inputs must be of uniform size and format.  10:31 Lois: And does each AI system have a different way of preparing data?  Yunus: For machine learning ML, focus is on cleaning, encoding, and scaling. Deep learning needs resizing and normalization for text and images. Data science, about reshaping, aggregating, and getting it ready for insights. The generative AI needs special preparation like chunking, tokenizing large documents, or compressing images. 11:06 Oracle University's Race to Certification 2025 is your ticket to free training and certification in today's hottest tech. Whether you're starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That's education.oracle.com/race-to-certification-2025. 11:50 Nikita: Welcome back! Yunus, how does a user choose the right model to solve their business problem?  Yunus: Just like a business uses different dashboards for marketing versus finance, in AI, we use different model types, depending on what we are trying to solve. Like classification is choosing a category. Real-world example can be whether the email is a spam or not. Use in fraud detection, medical diagnosis, et cetera. So what you do is you classify that particular data and then accurately access that classification of data. Regression, which is used for predicting a number, like, what will be the price of a house next month? Or it can be a useful in common forecasting sales demands or on the cost. Clustering, things without labels. So real-world examples can be segmenting customers based on behavior for targeted marketing. It helps discovering hidden patterns in large data sets.  Generation, that is creating new content. So AI writing product description or generating images can be a real-world example for this. And it can be used in a concept of generative AI models like ChatGPT or Dall-E, which operates on the generative AI principles. 13:16 Nikita: And how do you train a model? Yunus: We feed it with data in small chunks or batches and then compare its guesses to the correct values, adjusting its thinking like weights to improve next time, and the cycle repeats until the model gets good at making predictions. So if you're building a fraud detection system, ML may be enough. If you want to analyze medical images, you will need deep learning. If you're building a chatbot, go for a generative model like the LLM. And for all of these use cases, you need to select and train the applicable models as and when appropriate. 14:04 Lois: OK, now that the model's been trained, what else needs to happen before it can be deployed? Yunus: Evaluate the model, assess a model's accuracy, reliability, and real-world usefulness before it's put to work. That is, how often is the model right? Does it consistently perform well? Is it practical in the real world to use this model or not? Because if I have bad predictions, doesn't just look bad, it can lead to costly business mistakes. Think of recommending the wrong product to a customer or misidentifying a financial risk.  So what we do here is we start with splitting the data into two parts. So we train the data by training data. And this is like teaching the model. And then we have got the testing data. This is actually used for checking how well the model has learned. So once trained, the model makes predictions. We compare the predictions to the actual answers, just like checking your answer after a quiz. We try to go in for tailored evaluation based on AI types. Like machine learning, we care about accuracy in prediction. Deep learning is about fitting complex data like voice or images, where the model repeatedly sees examples and tunes itself to reduce errors. Data science, we look for patterns and insights, such as which features will matter. In generative AI, we judge by output quality. Is it coherent, useful, and is it natural?  The model improves with the accuracy and the number of epochs the training has been done on.  15:59 Nikita: So, after all that, we finally come to deploying the model… Yunus: Deploying a model means we are integrating it into our actual business system. So it can start making decisions, automating tasks, or supporting customer experiences in real time. Think of it like this. Training is teaching the model. Evaluating is testing it. And deployment is giving it a job.  The model needs a home either in the cloud or inside your company's own servers. Think of it like putting the AI in place where it can be reached by other tools. Exposed via API or embedded in an app, or you can say application, this is how the AI becomes usable.  Then, we have got the concept of receives live data and returns predictions. So receives live data and returns prediction is when the model listens to real-time inputs like a user typing, or user trying to search or click or making a transaction, and then instantly, your AI responds with a recommendation, decisions, or results. Deploying the model isn't the end of the story. It is just the beginning of the AI's real-world journey. Models may work well on day one, but things change. Customer behavior might shift. New products get introduced in the market. Economic conditions might evolve, like the era of COVID, where the demand shifted and the economical conditions actually changed. 17:48 Lois: Then it's about monitoring and improving the model to keep things reliable over time. Yunus: The monitor and improve loop is a continuous process that ensures an AI model remains accurate, fair, and effective after deployment. The live predictions, the model is running in real time, making decisions or recommendations. The monitor performance are those predictions still accurate and helpful. Is latency acceptable? This is where we track metrics, user feedbacks, and operational impact. Then, we go for detect issues, like accuracy is declining, are responses feeling biased, are customers dropping off due to long response times? And the next step will be to reframe or update the model. So we add fresh data, tweak the logic, or even use better architectures to deploy the uploaded model, and the new version replaces the old one and the cycle continues again. 18:58 Lois: And are there challenges during this step? Yunus: The common issues, which are related to monitor and improve consist of model drift, bias, and latency of failures. In model drift, the model becomes less accurate as the environment changes. Or bias, the model may favor or penalize certain groups unfairly. Latency or failures, if the model is too slow or fails unpredictably, it disrupts the user experience. Let's take the loan approvals. In loan approvals, if we notice an unusually high rejection rate due to model bias, we might retrain the model with more diverse or balanced data. For a chatbot, we watch for customer satisfaction, which might arise due to model failure and fine-tune the responses for the model. So in forecasting demand, if the predictions no longer match real trends, say post-pandemic, due to the model drift, we update the model with fresh data.  20:11 Nikita: Thanks for that, Yunus. Any final thoughts before we let you go? Yunus: No matter how advanced your model is, its effectiveness depends on the quality of the data you feed it. That means, the data needs to be clean, structured, and relevant. It should map itself to the problem you're solving. If the foundation is weak, the results will be also. So data preparation is not just a technical step, it is a business critical stage. Once deployed, AI systems must be monitored continuously, and you need to watch for drops in performance for any bias being generated or outdated logic, and improve the model with new data or refinements. That's what makes AI reliable, ethical, and sustainable in the long run. 21:09 Nikita: Yunus, thank you for this really insightful session. If you're interested in learning more about the topics we discussed today, go to mylearn.oracle.com and search for the AI for You course.  Lois: That's right. You'll find skill checks to help you assess your understanding of these concepts. In our next episode, we'll discuss the idea of buy versus build in the context of AI. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 21:39 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Adatépítész -az első magyar datapodcast Minden ami hír, érdekesség, esemény vagy tudásmorzsa az  adat, datascience, adatbányászat és hasonló kockaságok világából. Become a Patron! UPDATE: az Adás felvétele óta eltelt időben sem kaptam a cikk szerzőitől semmiféle választ az érdeklődésemre.

Twenty years after a scrappy newsroom team hacked together a framework to ship stories fast, Django remains the Python web framework that ships real apps, responsibly. In this anniversary roundtable with its creators and long-time stewards: Simon Willison, Adrian Holovaty, Will Vincent, Jeff Triplet, and Thibaud Colas, we trace the path from the Lawrence Journal-World to 1.0, DjangoCon, and the DSF; unpack how a BSD license and a culture of docs, tests, and mentorship grew a global community; and revisit lessons from deployments like Instagram. We talk modern Django too: ASGI and async, HTMX-friendly patterns, building APIs with DRF and Django Ninja, and how Django pairs with React and serverless without losing its batteries-included soul. You'll hear about Django Girls, Djangonauts, and the Django Fellowship that keep momentum going, plus where Django fits in today's AI stacks. Finally, we look ahead at the next decade of speed, security, and sustainability. Episode sponsors Talk Python Courses Python in Production Links from the show Guests Simon Willison: simonwillison.net Adrian Holovaty: holovaty.com Will Vincent: wsvincent.com Jeff Triplet: jefftriplett.com Thibaud Colas: thib.me Show Links Django's 20th Birthday Reflections (Simon Willison): simonwillison.net Happy 20th Birthday, Django! (Django Weblog): djangoproject.com Django 2024 Annual Impact Report: djangoproject.com Welcome Our New Fellow: Jacob Tyler Walls: djangoproject.com Soundslice Music Learning Platform: soundslice.com Djangonaut Space Mentorship for Django Contributors: djangonaut.space Wagtail CMS for Django: wagtail.org Django REST Framework: django-rest-framework.org Django Ninja API Framework for Django: django-ninja.dev Lawrence Journal-World: ljworld.com Watch this episode on YouTube: youtube.com Episode #518 deep-dive: talkpython.fm/518 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

This month, we explore how data science and AI are transforming the wine industry—from vineyard planting and grape harvesting to customer engagement. Can advanced technologies help winemakers enhance quality, promote sustainability, and better match wines to consumers—all while preserving the essential human touch? Might these innovations be applied to other products as well? Join us as we discuss these questions and more with industry leaders Kia Behnia, CEO and co-founder of Scout, and Katerina Axelsson, CEO and founder of Tastry. Pour yourself a glass and tune in as we uncork the intersection of data, AI, and the art of winemaking. Our Guests: Kia Behnia is CEO and co-founder of Scout, an AI-powered analytics platform built for precision viticulture, and proprietor of Kiatra Vineyards and Neotempo Wines.  Katerina Axelsson is CEO and founder of Tastry, a sensory-sciences company that blends advanced analytical chemistry, machine learning, and AI to predict consumer preferences—especially in wine.

A nostalgic dive into the rise and fall of true hacker culture - from MIT's curious tinkerers to today's hustle-obsessed "founders." Plus, why IRC was peak internet and what we lost when convenience killed community. For anyone who misses when coding was about elegance, not exits.RetryClaude can make mistakes. Please double-check responses. Interesting link https://www.twitch.tv/tsoding/about Sponsors DSH is proudly sponsored by Amethix Technologies. At the intersection of ethics and engineering, Amethix creates AI systems that don't just function—they adapt, learn, and serve. With a focus on dual-use innovation, Amethix is shaping a future where intelligent machines extend human capability, not replace it. Discover more at amethix.com   DSH is brought to you by Intrepid AI. From drones to satellites, Intrepid AI gives engineers and defense innovators the tools to prototype, simulate, and deploy autonomous systems with confidence. Whether it's in the sky, on the ground, or in orbit—if it's intelligent and mobile, Intrepid helps you build it. Learn more at intrepid.ai     ✨ Connect with us!

A statistician walks into a bar, and a comedy and art show begins. Creative work for scholars can extend beyond novel research and application. In today's episode of stats and stories, we see how the intersection between interest in statistics and art, as well as the intersection of statistics and comedy, with Dr Greg Matthews. Dr. Matthews is Associate Professor of Statistics and Director of the Center for Data Science and Consulting at Loyola University. He also is a data artist who developed and promoted the Data Art Show, which debuted at the 2016 Joint Statistical Meetings. He performs with the Uncontrolled Variables comedy troupe at the Lincoln Lodge in Chicago and you can see his data art, links to his comedy performance, and much more at his website, Stats in the Wild.

AI in software development sounds like a dream, faster coding, cleaner refactoring, and technical reports that actually make sense to stakeholders. But, what's the bad news in the classic good news/bad news scenario? Poisoned training data, compliance risks, and systems that are brittle and will not scale.  This week on Feds At The Edge, Alex Gromadzki, Assistant Director of Data Science at US GAO, and Steven Toy, Senior Director, Cloud Infrastructure for ICF, unpack the opportunities and pitfalls of generative AI in federal software development. From source-citing AI to data security in the software lifecycle, they reveal why small, testable use cases may be the smartest way forward.   Listen now on your favorite podcast platform to hear how federal leaders can balance innovation with responsibility as AI reshapes the software development life cycle.          

At National College of Ireland, we are dedicated to highlighting alternative entry routes into third level education. Students who have just received their Leaving Certificate results should be aware that the Available Places facility opens August 28th, 2025 What is Available Places? Available Places is a facility that highlights places that are still available on selected programmes for CAO Applicants. This means you can choose to study at NCI in September! There are a number of courses currently available on the Available Places facility, which can be viewed on our Available Places Courses page. Students interested in exploring NCI's Available Places options can explore areas of marketing, childhood education and care, as well as business and data science. How does Available Places benefit students? The Leaving Certificate year can be an uncertain time for many students. When all the stress of the exams has ended, some students may feel that they did not put a course down on their CAO choices that they should have, or perhaps when they have a better gauge of how they feel they did, they may wish they had have put some more choices down. When the Available Places facility opens, students have another opportunity to expand their CAO options or apply for courses they did not put down in the first place. For students who maybe did not get the exact results they were hoping for, the Available Places facility displays course options that may suit them. Regardless of how things played out on results day, the Available Places facility gives all students another opportunity to expand their study options for September. We hope that knowing that there is always another way in gives all students some peace of mind as they make their decisions about what and where they will study this September. You can view the NCI courses in Marketing Practice, Data Science, Business, Computing, and Early Childhood Education and Care that are available on our Available Places Courses page. Why Choose National College of Ireland? At National College of Ireland, we want to not only ensure that you meet your education ambitions, but we also want to ensure that your learning experience supports you personally through the services and supports we offer. Our mission is 'to change lives through education.' Choose your programme, and we will work with you to help you succeed. Located at the heart of the ISFC, NCI is one of Ireland's most innovative third-level institutions, we work closely with industry and professional bodies to ensure courses remain closely aligned to industry needs, and we welcome international students from all over the globe. We strive to continually provide a warm, welcoming, supportive environment so that all students can thrive in their academic, professional, and personal life. We look forward to welcoming new students this September. See more breaking stories here. More about Irish Tech News Irish Tech News are Ireland's No. 1 Online Tech Publication and often Ireland's No.1 Tech Podcast too. You can find hundreds of fantastic previous episodes and subscribe using whatever platform you like via our Anchor.fm page here: https://anchor.fm/irish-tech-news If you'd like to be featured in an upcoming Podcast email us at Simon@IrishTechNews.ie now to discuss. Irish Tech News have a range of services available to help promote your business. Why not drop us a line at Info@IrishTechNews.ie now to find out more about how we can help you reach our audience. You can also find and follow us on Twitter, LinkedIn, Facebook, Instagram, TikTok and Snapchat.

Rishard Rameez is the Co‑Founder and CEO of Zown, an AI‑powered real estate platform that makes homeownership more accessible and affordable. Zown was born from a viral Reddit post where Rishard shared his frustration over paying over $70K in real estate commissions. The outpouring of support inspired him to flip the model: instead of paying big commissions, Zown gives buyers significant upfront cash to help with their down payment and closing costs, while offering sellers flat fees. This customer‑first model has driven rapid growth, with Zown processing over $300 million in transactions and becoming Canada's fastest‑growing real estate brokerage. The platform has recently launched in California and continues expanding across North America. Rishard sparked a movement by transforming personal pain into an industry‑changing solution.(02:17) - The Broken Home Buying Process(03:02) - It All Started with a Viral Reddit Post (05:39) - Early Pivot from Flat Fee Model(14:59) - Unbundling Real Estate Services(18:06) - Feature: Blueprint - The Future of Real Estate - Register for 2025: The Premier Event for Industry Executives, Real Estate & Construction Tech Startups and VC's, at The Venetian, Las Vegas on Sep. 16th-18th, 2025.(19:00) - Feature: Meow - Business banking, with interest: Unlock a high-yield business checking account that pays up to 3.52%.(20:31) - Zone's Growth Journey(28:47) - Customer Acquisition Strategy(30:36) - Recent Seed Round(32:42) - Why Own vs. Rent a Home(34:52) - Collaboration Superpower: Muhammad and Jesus Christ

Celebrate a decade of innovation, learning, and connection in the Alteryx Community! In this special 10th anniversary episode of Alter Everything, we hear from you as we explore the stories and milestones that have defined the Alteryx Community over the past ten years. Hear firsthand accounts from users like you (maybe even you reading this) whose lives and careers have been transformed through mentorship, career advancement, or lifelong friendships. This episode highlights the power of Community in the world of data analytics. Join us as we honor the people, stories, and achievements that make the Alteryx Community truly special.Guests: Matt Rotundo, Engagement Engineer @Alteryx - @AlteryxMatt, LinkedInAlex Gross, Sr. Process Analyst @ Siemens - @grossal, LinkedInNicole Johnson, Sr. Manager Product Management @ Alteryx - @NicoleJ, LinkedInMatt Montgomery, Data Sherpa @ Montgomery Solutions - @mmontgomery, LinkedInCalvin Tang, Group Manager, Business Solutions & Enablement @ Prudential PLC - @Caltang, LinkedInSamantha Clifton, Sr. Sales Engineer @ Alteryx - @Samantha_Jayne, LinkedInLuke Cornetta, Sr. Director @ Alvarez and Marsal - @LukeC, LinkedInBen Stringer, Data Consultant @ Bulien - @BS_THE_ANALYST, LinkedInRoan Pilsworth, Data Consultant @ Bulien - @pilsner, LinkedInAlex Abi-Najm, Solutions and Enablement Lead @ Aimpoint Digital - @alexnajm, LinkedInShan Miralles, Quantitative Analyst @ JP Morgan Chase - @shancmiralles, LinkedInDan Menke, Community Ops Sr. Manager @ Alteryx - @DanM, LinkedInMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: Inspire ConferenceAlteryx ACE ProgramAdvent of CodeWeekly Challenges and Cloud QuestsAlteryx User GroupsAlteryx AcademyAlteryx Interactive LessonsAlteryx CertificationSparkEdRoad to Inspire Interested in sharing your feedback with the Alter Everything team? Take our feedback survey here!This episode was produced by Megan Bowers, Mike Cusic, and Matt Rotundo. Special thanks to Andy Uttley for the theme music.

The traditional career path of climbing the corporate ladder no longer appeals to many data scientists - who crave freedom and ownership of their work. Yet the leap from employment to independence can feel risky and uncertain, especially without a clear roadmap for success.In this episode, Daniel Bourke joins Dr. Genevieve Hayes to share his journey from machine learning engineer to successful independent data professional before age 30, revealing the practical steps and mindset shifts needed to transform technical skills into sustainable freedom.In this episode, you'll discover:Why embracing the "permissionless economy" is crucial for independent success [14:59]The power of "starting the job before you have it" [12:17]Why building your own website is the foundation for long-term independent success [24:35]A practical approach to opportunity selection that accelerates career momentum [17:27]Guest BioDaniel Bourke is the co-creator of Nutrify, an app described as “Shazam for food”, and teaches machine learning and deep learning at the Zero to Mastery Academy.LinksDaniel's websiteDaniel's YouTube channelConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Send us a textAI-Driven Business Transformation with Hitachi SolutionsThe Evolving Role of Data Science in AI: Hosted by Laurel Greszler, she visits with Hitachi Solutions Data Science & AI Resident Experts Fred Heller and Mike Kirkpatrick to discuss how data science is foundational to unlocking business value from vast, underutilized data assets. They emphasize that data scientists transform raw data into actionable insights, enabling organizations to automate and optimize decision-making processes. AI as a Strategic Business Enabler: The team highlights that AI, particularly generative AI (GenAI), is not a magic solution but a powerful tool that, when applied thoughtfully, accelerates business outcomes. They stress the importance of setting realistic expectations and integrating AI into existing analytical best practices. Three Core AI Solution Categories:Custom AI Chatbots: Tailored chatbots using Retrieval Augmented Generation (RAG) architectures allow dynamic interrogation of large text datasets, providing users with precise, context-aware answers. This approach leverages Azure cloud services for security and scalability, aligning with Microsoft investments. Document Processing Automation: AI-driven document processing dramatically reduces manual effort, cycle times, and costs by automating the extraction and summarization of information from large volumes of documents. This is especially impactful in industries burdened by compliance and documentation requirements. AI-Powered Document Generation: Generative AI can rapidly produce high-quality first drafts of complex documents (e.g., proposals, reports), giving teams a significant head start and freeing up time for higher-value work. This solution is particularly valuable for organizations that produce lengthy, formulaic documents. Custom vs. Out-of-the-Box Solutions: The conversation distinguishes between out-of-the-box tools like Microsoft Copilot and custom AI builds. While standard solutions offer quick wins, custom builds are essential for complex, industry-specific needs, offering better long-term value and integration with existing business processes. Industry-Agnostic Impact: The team shares that these AI solutions are applicable across industries—from manufacturing to pharmaceuticals—wherever there is a need to process or generate large volumes of information. The common thread is the desire to reduce manual reading and writing, improve efficiency, and empower employees to focus on higher-value tasks. Customer-Centric Approach: Hitachi Solutions' methodology starts with understanding the customer's true business challenge, ensuring that AI solutions are tailored to deliver measurable impact. Advisory workshops and collaborative design sessions are used to align technology with business goals. Microsoft Azure Expertise: As a Microsoft-dedicated partner, Hitachi Solutions leverages Azure's robust AI and data services to deliver secure, scalable, and future-ready solutions, ensuring customers maximize their existing technology investments. Key Takeaway: AI and data science, when strategically applied, can transform business operations, reduce costs, and enhance employee satisfaction. Hitachi Solutions offers deep expertise in custom AI development, ensuring solutions are both innovative and aligned with each customer's unique needs. global.hitachi-solutions.com

Topics covered in this episode: * pypistats.org was down, is now back, and there's a CLI* * State of Python 2025* * wrapt: A Python module for decorators, wrappers and monkey patching.* pysentry Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: pypistats.org was down, is now back, and there's a CLI pypistats.org is a cool site to check the download stats for Python packages. It was down for a while, like 3 weeks? A couple days ago, Hugo van Kemenade announced that it was back up. With some changes in stewardship “pypistats.org is back online!

We're bringing back one of our most downloaded episodes ever – a deep dive into how adverse events should be analyzed properly. This conversation with Jan Beyersmann and Kaspar Rufibach is packed with methodological insights and practical implications for statisticians working in clinical trials. Adverse event (AE) analysis has long been approached differently from efficacy analysis, often using overly simplistic methods that can bias results. In this episode, we discuss why that's a problem – and how the SAVVY collaboration (Survival analysis for AdVerse events with Varying follow-up times) is pushing the field forward. Together with academia and multiple pharma companies, this collaboration tackled the issue of AE analysis using real randomized trial data, not just simulations. The findings show how common methods can underestimate or overestimate event probabilities and how established statistical methods can be applied more consistently to ensure fair benefit–risk assessments. If you've ever wondered whether your approach to safety analysis is leading to misleading conclusions, this episode is a must-listen.

Co-taught by Prof. Wade Fagen-Ulmschneider (Grainger Engineering) and Prof. Karle Flanagan (Statistics), their CS/STAT 107 Data Science Discovery class is transforming the way students from 90+ majors approach data science. What's the secret to its wild success? - Real-world datasets - Hands-on, project-driven learning - Discovering how every major—from History to Finance—can be empowered by data! - And a wave of new X + DS degree programs putting Illinois students years ahead in the job market. Want to see where the future of education is headed? Hit play and dive into the one of the most exciting courses at Illinois— Data Science Discovery. 🔗 Explore more at: datasciencediscovery.org #DataScience #UIUC #IllinoisEngineering #Statistics #EducationInnovation #XplusDS #CSSTAT107 #CollegeMajors #CareerReady #DigitalFuture #StudentSuccess #BigTenChampions #STEM #HigherEd #MicroCredentials #pythonprogramming

Agentic AI programming is what happens when coding assistants stop acting like autocomplete and start collaborating on real work. In this episode, we cut through the hype and incentives to define “agentic,” then get hands-on with how tools like Cursor, Claude Code, and LangChain actually behave inside an established codebase. Our guest, Matt Makai, now VP of Developer Relations at DigitalOcean, creator of Full Stack Python and Plushcap, shares hard-won tactics. We unpack what breaks, from brittle “generate a bunch of tests” requests to agents amplifying technical debt and uneven design patterns. Plus, we also discuss a sane git workflow for AI-sized diffs. You'll hear practical Claude tips, why developers write more bugs when typing less, and where open source agents are headed. Hint: The destination is humans as editors of systems, not just typists of code. Episode sponsors Posit Talk Python Courses Links from the show Matt Makai: linkedin.com Plushcap Developer Content Analytics: plushcap.com DigitalOcean Gradient AI Platform: digitalocean.com DigitalOcean YouTube Channel: youtube.com Why Generative AI Coding Tools and Agents Do Not Work for Me: blog.miguelgrinberg.com AI Changes Everything: lucumr.pocoo.org Claude Code - 47 Pro Tips in 9 Minutes: youtube.com Cursor AI Code Editor: cursor.com JetBrains Junie: jetbrains.com Claude Code by Anthropic: anthropic.com Full Stack Python: fullstackpython.com Watch this episode on YouTube: youtube.com Episode #517 deep-dive: talkpython.fm/517 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Dr. Rebecca Portnoff generates awareness of the threat landscape, enablers, challenges and solutions to the complex but addressable issue of online child sexual abuse.  Rebecca and Kimberly discuss trends in online child sexual abuse; pillars of impact and harm; how GenAI expands the threat landscape; personalized targeting and bespoke abuse; Thorn's Safety by Design Initiative; scalable prevention strategies; technical and legal barriers; standards, consensus and commitment; building better from the beginning; accountability as an innovative goal; and not confusing complex with unsolvable.  Dr. Rebecca Portnoff is the Vice President of Data Science at Thorn, a non-profit dedicated to protecting children from sexual abuse. Read Thorn's seminal Safety by Design paper, bookmark the Research Center to stay updated and support Thorn's critical work by donating here. Related Resources Thorn's Safety by Design Initiative (News): https://www.thorn.org/blog/generative-ai-principles/  Safety by Design Progress Reports: https://www.thorn.org/blog/thorns-safety-by-design-for-generative-ai-progress-reports/  Thorn + SIO AIG-CSAM Research (Report): https://cyber.fsi.stanford.edu/io/news/ml-csam-report  A transcript of this episode is here.

To mark our 50th episode, host Ross Katz brings back three visionary leaders—Dave Johnson (Dash Bio), Wolfgang Halter (Merck Life Science), and Jacob Oppenheim (RAVen)—together for a reflection on the evolution of biotech. They unpack the realities behind AI hype, the future of data-driven innovation, and what's really changing in drug development. ​​What You'll Learn in This Episode: >> Where real innovation is emerging across startups, big tech, and academia >> The biggest misconceptions about data in biotech—and why they persist >> What it takes to build trust in AI-powered biotech tools >> Why progress in biotech depends as much on execution as it does on breakthroughs >> How industry veterans see the future of automation, regulation, and global competition Meet Our Guests Dave Johnson is CEO and Co-Founder of Dash Bio and former Chief Data & AI Officer at Moderna. He's pioneering automation in clinical bioanalysis to accelerate drug development. Wolfgang Halter leads Data Science at Merck Life Science, developing tools like BayBE to optimize R&D through smarter data modeling and open-source innovation. Jacob Oppenheim is a Venture Partner at RAVen and co-founder of Fresnel. With a PhD in Biological Physics, he champions the transition to digital-native biopharma. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Sponsor: CorrDyn, a data consultancyConnect with Dave Johnson  on LinkedIn Connect with Wolfgang Halter on LinkedInConnect with Jacob Oppenheim on LinkedIn Connect with Us: Follow the podcast for more insightful discussions on the latest in biotech and data science.Subscribe and leave a review if you enjoyed this episode!Connect with Ross Katz on LinkedIn Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn.

Misfits Makin' It is the podcast component of the misfit comedy shows produced by Lauren LoGiudice. Show dates and info at www.laurenlogiudice.com In this Misfit Melodrama mini-episode of Misfits Makin' It Lauren speaks with scientist and comedian Andrea Jones-Rooy. The conversation explores the pressures to specialize, the importance of interdisciplinary thinking, and the realities of navigating comedy and performance as a misfit. Andrea shares insights on self-acceptance, resilience, and finding fulfillment by embracing multiple passions rather than conforming to traditional expectations. To submit your story leave a voicemail at 646-WANG-0-X-1 or go to www.laurenlogiudice.com/podcast. HOW TO SUPPORT THE PODCAST: Rate and review: Misfits trust other misfits to tell them what is good! Tell a friend: Word of mouth is the #1 way misfits learn about their next pod. Sponsor a podcast: Affordable for individuals and small businesses, also makes the perfect gift. Support this art directly with a podcast that's custom-tailored to you or your friends. Make it happen by reaching out to inthemidstprod@gmail.com. CONNECT WITH ANDREA JONES-ROOY: www.jonesrooy.com Instagram: @jonesrooy HOW TO SUPPORT THE PODCAST: Rate and review: Misfits trust other misfits to tell them what is good! Tell a friend: Work of mouth is the #1 way misfits like to learn about their next pod. Sponsor a podcast: Affordable for individuals and small businesses, also makes the perfect gift. Support this art directly with a podcast that's custom-tailored to you or your friends. Make it happen by reaching out to inthemidstprod@gmail.com. CONNECT WITH LAUREN LOGIUDICE: Instagram: @laurenlogi Twitter/TikTok/Threads: @laurenlogi Website: www.laurenlogiudice.com For more about the Honestly crowdfunding campaign visit: https://seedandspark.com/fund/honestly#story

The right book at the right time can completely transform your career trajectory, but many data professionals struggle to find resources that directly address their unique challenges of bridging technical expertise with business impact. While technical skills courses are abundant, guidance on becoming a strategic data leader remains scarce.In this Value Boost episode, Kashif Zahoor joins Dr. Genevieve Hayes to reveal how he transformed his entire data team's performance and culture through a simple but powerful approach: starting a BI book club that costs almost nothing but delivers enormous ROI.This episode reveals:How a weekly team book club transformed Kashif's data team [02:26]The "data concierge" concept that transforms dashboard builders into trusted business advisors [04:07]Why Data Insights Delivered by Mo Villagran is a team game-changer [08:28]The critical difference between fulfilling requests and solving underlying business problems [09:05]Guest BioKashif Zahoor is the Vice President of Business Intelligence at Influence Mobile and has extensive experience in data leadership.LinksConnect with Kashif on LinkedInData Insights Delivered (Amazon Australia)(Amazon US)The AI-Driven Leader (Amazon Australia)(Amazon US)Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Python's data stack is getting a serious GPU turbo boost. In this episode, Ben Zaitlen from NVIDIA joins us to unpack RAPIDS, the open source toolkit that lets pandas, scikit-learn, Spark, Polars, and even NetworkX execute on GPUs. We trace the project's origin and why NVIDIA built it in the open, then dig into the pieces that matter in practice: cuDF for DataFrames, cuML for ML, cuGraph for graphs, cuXfilter for dashboards, and friends like cuSpatial and cuSignal. We talk real speedups, how the pandas accelerator works without a rewrite, and what becomes possible when jobs that used to take hours finish in minutes. You'll hear strategies for datasets bigger than GPU memory, scaling out with Dask or Ray, Spark acceleration, and the growing role of vector search with cuVS for AI workloads. If you know the CPU tools, this is your on-ramp to the same APIs at GPU speed. Episode sponsors Posit Talk Python Courses Links from the show RAPIDS: github.com/rapidsai Example notebooks showing drop-in accelerators: github.com Benjamin Zaitlen - LinkedIn: linkedin.com RAPIDS Deployment Guide (Stable): docs.rapids.ai RAPIDS cuDF API Docs (Stable): docs.rapids.ai Asianometry YouTube Video: youtube.com cuDF pandas Accelerator (Stable): docs.rapids.ai Watch this episode on YouTube: youtube.com Episode #516 deep-dive: talkpython.fm/516 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Topics covered in this episode: pyx - optimized backend for uv * Litestar is worth a look* * Django remake migrations* * django-chronos* Extras Joke Watch on YouTube About the show Python Bytes 445 Sponsored by Sentry: pythonbytes.fm/sentry - Python Error and Performance Monitoring Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: pyx - optimized backend for uv via John Hagen (thanks again) I'll be interviewing Charlie in 9 days on Talk Python → Sign up (get notified) of the livestream here. Not a PyPI replacement, more of a middleware layer to make it better, faster, stronger. pyx is a paid service, with maybe a free option eventually. Brian #2: Litestar is worth a look James Bennett Michael brought up Litestar in episode 444 when talking about rewriting TalkPython in Quart James brings up scaling - Litestar is easy to split an app into multiple files Not using pydantic - You can use pydantic with Litestar, but you don't have to. Maybe attrs is right for you instead. Michael brought up Litestar seems like a “more batteries included” option. Somewhere between FastAPI and Django. Brian #3: Django remake migrations Suggested by Bruno Alla on BlueSky In response to a migrations topic last week django-remake-migrations is a tool to help you with migrations and the docs do a great job of describing the problem way better than I did last week “The built-in squashmigrations command is great, but it only work on a single app at a time, which means that you need to run it for each app in your project. On a project with enough cross-apps dependencies, it can be tricky to run.” “This command aims at solving this problem, by recreating all the migration files in the whole project, from scratch, and mark them as applied by using the replaces attribute.” Also of note The package was created with Copier Michael brought up Copier in 2021 in episode 219 It has a nice comparison table with CookieCutter and Yoeman One difference from CookieCutter is yml vs json. I'm actually not a huge fan of handwriting either. But I guess I'd rather hand write yml. So I'm thinking of trying Copier with my future project template needs. Michael #4: django-chronos Django middleware that shows you how fast your pages load, right in your browser. Displays request timing and query counts for your views and middleware. Times middleware, view, and total per request (CPU and DB). Extras Brian: Test & Code 238: So Long, and Thanks for All the Fish after 10 years, this is the goodbye episode Michael: Auto-activate Python virtual environment for any project with a venv directory in your shell (macOS/Linux): See gist. Python 3.13.6 is out. Open weight OpenAI models Just Enough Python for Data Scientists Course The State of Python 2025 article by Michael Joke: python is better than java

Bob Rudis, VP Data Science from GreyNoise, is sharing some insights into their work on "Early Warning Signals: When Attacker Behavior Precedes New Vulnerabilities." New research reveals a striking trend: in 80% of cases, spikes in malicious activity against enterprise edge technologies like VPNs and firewalls occurred weeks before related CVEs were disclosed. The report breaks down this “6-week critical window,” highlighting which vendors show the strongest early-warning patterns and offering tactical steps defenders can take when suspicious spikes emerge. These findings reveal how early attacker activity can be transformed into actionable intelligence, enabling defenders to anticipate and neutralize threats before vulnerabilities are publicly disclosed. Complete our annual ⁠⁠⁠audience survey⁠⁠⁠ before August 31. The research can be found here: Early Warning Signals: When Attacker Behavior Precedes New Vulnerabilities Learn more about your ad choices. Visit megaphone.fm/adchoices

A young computer scientist and two colleagues show that searches within data structures called hash tables can be much faster than previously deemed possible. The story How Undergraduate Upends a 40-Year-Old Data Science Conjecture first appeared on Quanta Magazine.

In this episode of Elixir Wizards, host Sundi Myint chats with SmartLogic engineers and fellow Wizards Dan Ivovich and Charles Suggs about the practical tooling that surrounds Elixir in a consultancy setting. We dig into how standardized dev environments, sensible scaffolding, and clear observability help teams ship quickly across many client projects without turning every app into a snowflake. Join us for a grounded tour of what's working for us today (and what we've retired), plus how we evaluate new tech (including AI) through a pragmatic, Elixir-first lens. Key topics discussed in this episode: Standardizing across projects: why consistent environments matter in consultancy work Nix (and flakes) for reproducible dev setups and faster onboarding Igniter to scaffold common patterns (auth, config, workflows) without boilerplate drift Deployment approaches: OTP releases, runtime config, and Ansible playbooks Frontend pipeline evolution: from Brunch/Webpack to esbuild + Tailwind Observability in practice: Prometheus metrics and Grafana dashboards Handling time-series and sensor data When Explorer can be the database Picking the right tool: Elixir where it shines, integrations where it counts Using AI with intention: code exploration, prototypes, and guardrails for IP/security Keeping quality high across multiple codebases: tests, telemetry, and sensible conventions Reducing context-switching costs with shared patterns and playbooks Links mentioned: http://smartlogic.io https://nix.dev/ https://github.com/ash-project/igniter Elixir Wizards S13E01 Igniter with Zach Daniel https://youtu.be/WM9iQlQSFg https://github.com/elixir-explorer/explorer Elixir Wizards S14E09 Explorer with Chris Grainger https://youtu.be/OqJDsCF0El0 Elixir Wizards S14E08 Nix with Norbert (Nobbz) Melzer https://youtu.be/yymUcgy4OAk https://jqlang.org/ https://github.com/BurntSushi/ripgrep https://github.com/resources/articles/devops/ci-cd https://prometheus.io/ https://capistranorb.com/ https://ansible.com/  https://hexdocs.pm/phoenix/releases.html https://brunch.io/ https://webpack.js.org/loaders/css-loader/ https://tailwindcss.com/ https://sass-lang.com/dart-sass/ https://grafana.com/ https://pragprog.com/titles/passweather/build-a-weather-station-with-elixir-and-nerves/ https://www.datadoghq.com/ https://sqlite.org/ Elixir Wizards S14E06 SDUI at Cars.com with Zack Kayser https://youtu.be/nloRcgngTk https://github.com/features/copilot https://openai.com/codex/ https://www.anthropic.com/claude-code YouTube Video: Vibe Coding TEDCO's RFP https://youtu.be/i1ncgXZJHZs Blog: https://smartlogic.io/blog/how-i-used-ai-to-vibe-code-a-website-called-for-in-tedco-rfp/ Blog: https://smartlogic.io/blog/from-vibe-to-viable-turning-ai-built-prototypes-into-market-ready-mvps/ https://www.thriftbooks.com/w/eragon-by-christopher-paolini/246801 https://tidewave.ai/ !! We Want to Hear Your Thoughts *!!* Have questions, comments, or topics you'd like us to discuss in our season recap episode? Share your thoughts with us here: https://forms.gle/Vm7mcYRFDgsqqpDC9

What if your code was crash-proof? That's the value prop for a framework called Temporal. Temporal is a durable execution platform that enables developers to build scalable applications without sacrificing productivity or reliability. The Temporal server executes units of application logic called Workflows in a resilient manner that automatically handles intermittent failures, and retries failed operations. We have Mason Egger from Temporal on to dive into durable execution. Episode sponsors Posit PyBay Talk Python Courses Links from the show Just Enough Python for Data Scientists Course: talkpython.fm Temporal Durable Execution Platform: temporal.io Temporal Learn Portal: learn.temporal.io Temporal GitHub Repository: github.com Temporal Python SDK GitHub Repository: github.com What Is Durable Execution, Temporal Blog: temporal.io Mason on Bluesky Profile: bsky.app Mason on Mastodon Profile: fosstodon.org Mason on Twitter Profile: twitter.com Mason on LinkedIn Profile: linkedin.com X Post by @skirano: x.com Temporal Docker Compose GitHub Repository: github.com Building a distributed asyncio event loop (Chad Retz) - PyTexas 2025: youtube.com Watch this episode on YouTube: youtube.com Episode #515 deep-dive: talkpython.fm/515 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Topics covered in this episode: Coverage.py regex pragmas * Python of Yore* * nox-uv* * A couple Django items* Extras Joke Watch on YouTube About the show Sponsored by DigitalOcean: pythonbytes.fm/digitalocean-gen-ai Use code DO4BYTES and get $200 in free credit Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Coverage.py regex pragmas Ned Batchelder The regex implementation of how coverage.py recognizes pragmas is pretty amazing. It's extensible through plugins covdefaults adds a bunch of default exclusions, and also platform- and version-specific comment syntaxes. coverage-conditional-plugin gives you a way to create comment syntaxes for entire files, for whether other packages are installed, and so on. A change from last year (as part of coverage.py 7.6 allows multiline regexes, which let's us do things like: Exclude an entire file with A(?s:.*# pragma: exclude file.*)Z Allow start and stop delimiters with # no cover: start(?s:.*?)# no cover: stop Exclude empty placeholder methods with ^s*(((async )?def .*?)?)(s*->.*?)?:s*)?...s*(#|$) See Ned's article for explanations of these Michael #2: Python of Yore via Matthias Use YORE: ... comments to highlight CPython version dependencies. # YORE: EOL 3.8: Replace block with line 4. if sys.version_info < (3, 9): from astunparse import unparse else: from ast import unparse Then check when they go out of support: $ yore check --eol-within '5 months' ./src/griffe/agents/nodes/_values.py:11: Python 3.8 will reach its End of Life within approx. 4 months Even fix them with fix . Michael #3: nox-uv via John Hagen What nox-uv does is make it very simple to install uv extras and/or dependency groups into a nox session's virtual environment. The versions installed are constrained by uv's lockfile meaning that everything is deterministic and pinned. Dependency groups make it very easy to install only want is necessary for a session (e.g., only linting dependencies like Ruff, or main dependencies + mypy for type checking). Brian #4: A couple Django items Stop Using Django's squashmigrations: There's a Better Way Johnny Metz Resetting migrations is sometimes the right thing. Overly simplified summary: delete migrations and start over dj-lite Adam Hill Use SQLite in production with Django “Simplify deploying and maintaining production Django websites by using SQLite in production. dj-lite helps enable the best performance for SQLite for small to medium-sized projects. It requires Django 5.1+.” Extras Brian: Test & Code 237: FastAPI Cloud with Sebastian Ramirez will be out later today pythontest.com: pytest fixtures nuts and bolts - revisited A blog series that I wrote a long time ago. I've updated it into more managable bite-sized pieces, updated and tested with Python 3.13 and pytest 8 Michael: New course: Just Enough Python for Data Scientists My live stream about uv is now on YouTube Cursor CLI: Built to help you ship, right from your terminal. Joke: Copy/Paste