Podcasts about Data science

Compensation isn't just an HR function—it's a core business strategy. In this episode of Comp and Coffee, we look into the pay crystal ball with Rebecca Toman of Pearl Meyer, Ruth Thomas, Chief Compensation Strategist at Payscale, and Sara Hillenmeyer, Payscale's Director of Data Science. Together, they explore how trusted data, AI, and a forward-thinking approach to comp strategy can help organizations not just keep up—but lead. If you care about performance, retention, equity, or executive pay, this episode is your blueprint for aligning comp strategy with long-term business success. 

Every year the core developers of Python convene in person to focus on high priority topics for CPython and beyond. This year they met at PyCon US 2025. Those meetings are closed door to keep focused and productive. But we're lucky that Seth Michael Larson was in attendance and wrote up each topic presented and the reactions and feedback to each. We'll be exploring this year's Language Summit with Seth. It's quite insightful to where Python is going and the pressing matters. Episode sponsors Seer: AI Debugging, Code TALKPYTHON Sentry AI Monitoring, Code TALKPYTHON Talk Python Courses Links from the show Seth on Mastodon: @sethmlarson@fosstodon.org Seth on Twitter: @sethmlarson Seth on Github: github.com Python Language Summit 2025: pyfound.blogspot.com WheelNext: wheelnext.dev Free-Threaded Wheels: hugovk.github.io Free-Threaded Python Compatibility Tracking: py-free-threading.github.io PEP 779: Criteria for supported status for free-threaded Python: discuss.python.org PyPI Data: py-code.org Senior Engineer tries Vibe Coding: youtube.com Watch this episode on YouTube: youtube.com Episode #514 deep-dive: talkpython.fm/514 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

In this episode, we're joined by Jackie (@agaperste), Head of Data Science at Uniswap Foundation, and Grace (@grace_lily), part of the Data team at Uniswap

Hablamos de tecnología e IA con Xavier Ferràs, profesor del departamento de Operaciones, Innovación y Data Science en ESADE. Tertulia con Francisco Navarro, profesor del Instituto de Empresa; Rubén García-Quismondo, Socio Director de Quabbala, Abogados y Economistas; y Gonzalo Garnica, consultor empresarial y editor.

Recebemos o Daniel Romeiro — mais conhecido como Infoslack — para mergulhar de cabeça no universo em ebulição de Inteligência Artificial, DevOps e Machine Learning. Neste episódio, exploramos como filtrar o ruído do hype com uma abordagem de filtro reverso e discutimos os bastidores do deploy de modelos de Machine Learning em produção.Trocamos experiências sobre observabilidade avançada em pipelines de IA e compartilhamos insights sobre como acumular habilidades DevOps ao longo da carreira, sem jamais perder o pé no chão. Entre uma piada e outra, analisamos também o impacto dos testes A/B em tempo real e a complexidade de gerenciar artefatos de IA em escala.Por fim, refletimos sobre as perspectivas futuras: qual será o próximo grande passo para SREs que querem continuar relevantes em um cenário dominado por IA generativa? Nós conversamos sobre como arquiteturas mal planejadas podem se tornar gargalos de latência e apresentamos estratégias para garantir alta disponibilidade mesmo quando as APIs externas decidem ficar fora do ar.Links Importantes:- Daniel Romeiro - https://www.linkedin.com/in/infoslack/- João Brito - https://www.linkedin.com/in/juniorjbn- Assista ao FilmeTEArapia - https://youtu.be/M4QFmW_HZh0?si=HIXBDWZJ8yPbpflMParticipe de nosso programa de acesso antecipado e tenha um ambiente mais seguro em instantes!https://getup.io/zerocveO Kubicast é uma produção da Getup, empresa especialista em Kubernetes e projetos open source para Kubernetes. Os episódios do podcast estão nas principais plataformas de áudio digital e no YouTube.com/@getupcloud.

Social media algorithms silently shape what billions of people see and how they interact online. While most data scientists work to optimize business value within platform rules, there's valuable knowledge to be gained from understanding how these systems can be exploited - knowledge that can make ethical data scientists better at their jobs.In this episode, Tim O'Hearn joins Dr. Genevieve Hayes to share insights from his experience manipulating social media platforms, revealing what ethical data scientists can learn from understanding the dark side of algorithmic systems.This conversation reveals:How social media platforms are essentially just sophisticated recommendation engines [08:16]The "canary" technique for detecting when underlying systems have changed [11:36]Why customer accounts often provide better testing data than artificial test accounts [13:56]The importance of time series data collection for identifying suspicious patterns, effectiveness of campaigns, and understanding platform dynamics [18:04]Guest BioTim O'Hearn is a software engineer who spent years gaining millions of followers for clients by circumventing anti-botting measures on social networks. He is also the author of the new book, Framed: A Villain's Perspective on Social Media.LinksTim's WebsiteConnect with Tim on LinkedInSubscribe to Tim's newsletterConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Welcome to the CanadianSME Small Business Podcast, hosted by Kripa Anand, where we explore cutting-edge technologies and ethical considerations shaping the future of business. Today, we're focusing on the transformative power of Artificial Intelligence in sales and the critical role of ethics and regulations in its application. As AI drives sales productivity and revenue growth, its responsible and ethical use becomes crucial for businesses seeking to leverage its full potential.Joining us today is Dr. Cindy Gordon, CEO & Founder of SalesChoice, a SaaS and Data Sciences company focused on enabling human advantage through Trusted AI Methods. Dr. Gordon is a leading voice in AI and ethics, and today we'll discuss the intersection of AI, ethics, and regulations in sales. Let's dive in!Key Highlights:1. AI, Ethics, Regulations, and the Application of All 3 in Sales: Dr. Gordon will explain how AI, ethics, and regulations are converging in the context of sales, and share the key considerations businesses need to ensure they're using AI responsibly and ethically in their sales processes.2. AI Applications in Sales: Dr. Gordon will discuss some of the most effective ways businesses can leverage AI to improve sales performance, increase revenue, and enhance sales team productivity.3. SalesChoice's AI Platform: InsightEngine™: Dr. Gordon will talk about SalesChoice's AI platform, including SalesInsights™ and MoodInsights™, and how they address different aspects of sales and employee productivity.4. Trusted AI Methods: Dr. Gordon will explain what “Trusted AI Methods” means in practice and why trust is so important in the adoption of AI solutions.5. AI Enablement Advisory and Strategy Solutions: Dr. Gordon will outline the services SalesChoice provides and how they help organizations across diverse AI use cases.Special Thanks to Our Partners:RBC: https://www.rbcroyalbank.com/dms/business/accounts/beyond-banking/index.htmlUPS: https://solutions.ups.com/ca-beunstoppable.html?WT.mc_id=BUSMEWAGoogle: https://www.google.ca/For more expert insights, visit www.canadiansme.ca and subscribe to the CanadianSME Small Business Magazine. Stay innovative, stay informed, and thrive in the digital age!Disclaimer: The information shared in this podcast is for general informational purposes only and should not be considered as direct financial or business advice. Always consult with a qualified professional for advice specific to your situation.

Topics covered in this episode: * Switching to direnv, Starship, and uv* * rqlite - Distributed SQLite DB* * Some Markdown Stuff* Extras Joke Watch on YouTube About the show Sponsored by PropelAuth: pythonbytes.fm/propelauth77 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: Switching to direnv, Starship, and uv Last week I mentioned that I'm ready to try direnv again, but secretly, I still had some worries about the process. Thankfully, Trey has a tutorial to walk me past the troublesome parts. direnv - an extension for your shell. It augments existing shells with a new feature that can load and unload environment variables depending on the current directory. Switching from virtualenvwrapper to direnv, Starship, and uv - Trey Hunner** Trey has solved a bunch of the problems I had when I tried direnv before Show the virtual environment name in the prompt Place new virtual environments in local .venv instead of in .direnv/python3.12 Silence all of the “loading”, “unloading” statements every time you enter a directory Have a script called venv to create an environment, activate it, create a .envrc file I'm more used to a create script, so I'll stick with that name and Trey's contents A workon script to be able to switch around to different projects. This is a carry over from “virtualenvwrapper', but seems cool. I'll take it. Adding uv to the mix for creating virtual environments. Interestingly including --seed which, for one, installs pip in the new environment. (Some tools need it, even if you don't) Starship Trey also has some setup for Starship. But I'll get through the above first, then MAYBE try Starship again. Some motivation Trey's setup is pretty simple. Maybe I was trying to get too fancy before Starship config in toml files that can be loaded with direnv and be different for different projects. Neato Also, Trey mentions his dotfiles repo. This is a cool idea that I've been meaning to do for a long time. See also: It's Terminal - Bootstrapping With Starship, Just, Direnv, and UV - Mario Munoz Michael #2: rqlite - Distributed SQLite DB via themlu, thanks! rqlite is a lightweight, user-friendly, distributed relational database built on SQLite. Built on SQLite, the world's most popular database Supports full-text search, Vector Search, and JSON documents Access controls and encryption for secure deployments Michael #3: A Python dict that can report which keys you did not use by Peter Bengtsson Very cool for testing that a dictionary has been used as expected (e.g. all data has been sent out via an API or report). Note: It does NOT track d.get(), but it's easy to just add it to the class in the post. Maybe someone should polish it up and put it on pypi (that person is not me :) ). Brian #4: Some Markdown Stuff Textual 4.0.0 adds Markdown.append which can be used to efficiently stream markdown content The reason for the major bump is due to an interface change to Widget.anchor Refreshing to see a symantic change cause a major version bump. html-to-markdown Converts html to markdown A complete rewrite fork of markdownify Lots of fun features like “streaming support” Curious if it can stream to Textual's Markdown.append method. hmmm. Joke: Vibecon is hard to attend

This is episode 298 recorded on July 9th, 2025, where John & Jason talk the Microsoft Fabric June 2025 Feature Summary including lots of Notebook updates in Data Engineering, lower cost for AI functions in Data Science, Copilot for RTI dashboards, and more. For show notes please visit www.bifocal.show

Why do people list to this podcast? Sure, they're looking for technical explorations of new libraries and ideas. But often it's to hear the story behind them. If that speaks to you, then I have the perfect episode lined up. I have Barry Warsaw, Paul Everitt, Carol Willing, and Brett Cannon all back on the show to share stories from the history of Python. You'll hear about how import this came to be and how the first PyCon had around 30 attendees (two of whom are guests on this episode!). Sit back and enjoy the humorous stories from Python's past. Episode sponsors Posit Agntcy Talk Python Courses Links from the show Barry's Zen of Python song: youtube.com Jake Vanderplas - Keynote - PyCon 2017: youtube.com Why it's called “Python” (Monty Python fan-reference): geeksforgeeks.org import antigravity: python-history.blogspot.com NIST Python Workshop Attendees: legacy.python.org Paul Everitt open-sources Zope: old.zope.dev Carol Willing wins ACM Software System Award: awards.acm.org Watch this episode on YouTube: youtube.com Episode #513 deep-dive: talkpython.fm/513 Episode transcripts: talkpython.fm --- 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

In this conversation, we explore AI bias, transformative justice, and the future of technology with Dr. Avriel Epps, computational social scientist, Civic Science Postdoctoral Fellow at Cornell University's CATLab, and co-founder of AI for Abolition.What makes this conversation unique is how it begins with Avriel's recently published children's book, A Kids Book About AI Bias (Penguin Random House), designed for ages 5-9. As an accomplished researcher with a PhD from Harvard and expertise in how algorithmic systems impact identity development, Avriel has taken on the remarkable challenge of translating complex technical concepts about AI bias into accessible language for the youngest learners.Key themes we explore:- The Translation Challenge: How to distill graduate-level research on algorithmic bias into concepts a six-year-old can understand—and why kids' unfiltered responses to AI bias reveal truths adults often struggle to articulate- Critical Digital Literacy: Why building awareness of AI bias early can serve as a protective mechanism for young people who will be most vulnerable to these systems- AI for Abolition: Avriel's nonprofit work building community power around AI, including developing open-source tools like "Repair" for transformative and restorative justice practitioners- The Incentive Problem: Why the fundamental issue isn't the technology itself, but the economic structures driving AI development—and how communities might reclaim agency over systems built from their own data- Generational Perspectives: How different generations approach digital activism, from Gen Z's innovative but potentially ephemeral protest methods to what Gen Alpha might bring to technological resistanceThroughout our conversation, Avriel demonstrates how critical analysis of technology can coexist with practical hope. Her work embodies the belief that while AI currently reinforces existing inequalities, it doesn't have to—if we can change who controls its development and deployment.The conversation concludes with Avriel's ongoing research into how algorithmic systems shaped public discourse around major social and political events, and their vision for "small tech" solutions that serve communities rather than extracting from them.For anyone interested in AI ethics, youth development, or the intersection of technology and social justice, this conversation offers both rigorous analysis and genuine optimism about what's possible when we center equity in technological development.About Dr. Avriel Epps:Dr. Avriel Epps (she/they) is a computational social scientist and a Civic Science Postdoctoral Fellow at the Cornell University CATLab. She completed her Ph.D. at Harvard University in Education with a concentration in Human Development. She also holds an S.M. in Data Science from Harvard's School of Engineering and Applied Sciences and a B.A. in Communication Studies from UCLA. Previously a Ford Foundation predoctoral fellow, Avriel is currently a Fellow at The National Center on Race and Digital Justice, a Roddenberry Fellow, and a Public Voices Fellow on Technology in the Public Interest with the Op-Ed Project in partnership with the MacArthur Foundation.Avriel is also the co-founder of AI4Abolition, a community organization dedicated to increasing AI literacy in marginalized communities and building community power with and around data-driven technologies. Avriel has been invited to speak at various venues including tech giants like Google and TikTok, and for The U.S. Courts, focusing on algorithmic bias and fairness. In the Fall of 2025, she will begin her tenure as Assistant Professor of Fair and Responsible Data Science at Rutgers University.Links:- Dr. Epps' official website: https://www.avrielepps.com- AI for Abolition: https://www.ai4.org- A Kids Book About AI Bias details: https://www.avrielepps.com/book

Listen as an experienced post -secondary professor and Dean of Computing and Data Science shares her insights as she assumes the position of the Head of School at a 6-12 independent school, the New England Innovation Academy. Dr. Durga Suresh-Menon provides a definition of innovation and discusses the need for curriculum and instruction to be continuously relevant. How do students prepare for what comes next?  How do staff engage in the learning that supports their work with student? Email Dr. Suresh-Menon: durga.suresh-menon@neiacademy.org Learn more about the New England Innovation Academy here.  Subscribe to the Steve Barkley Ponders Out Loud podcast on iTunes or visit BarkleyPD.com to find new episodes!  

Most dashboards and reports get ignored despite all the technical expertise that goes into creating them. The reason isn't technical limitations or poor data quality - it's that they fail to deliver value to the people who are supposed to use them.In this Value Boost episode, Nicholas Kelly joins Dr. Genevieve Hayes to reveal proven strategies for increasing dashboard adoption and showcasing your value as a data professional.In this episode, you'll discover:The number one reason why dashboards fail [01:15]The three-bucket framework that transforms dashboard development [04:06]How to salvage an already-built dashboard [07:12]The simple wireframing technique that opens doors to meaningful user conversations [10:08]Guest BioNicholas Kelly is the founder of Delivering Data Analytics, a consultancy focused on helping organisations enable their teams to make smarter, faster, and more confident decisions through data and AI. He is also the author of Delivering Data Analytics and the recently released How to Interpret Data.LinksNicholas's WebsiteConnect with Nicholas on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

All companies start small. The lucky ones grow, and growth necessarily comes with change. This month on the Klaviyo Data Science Podcast, we look at Klaviyo's growth through the past 8 years and the profound effects that growth has had on R&D, ranging from the tooling we use to the processes we employ to the very culture we strive to cultivate. Listen in to learn:How the day-to-day of some of our most senior engineers has changedWhy common headaches like planning, deployment, and cross-team coordination can get harder as your org grows — and why they can get easier What a single lunch can teach you about a company's culture For more details, check out the ⁠⁠full writeup on Medium⁠⁠!

In this insightful episode of IDEA Collider, Mike welcomes Mathai Mammen, Chairman and CEO of Parabilis Medicines. Mathai shares his extensive journey through academia, MD PhD program at Harvard, co-founding Theravance, and leadership roles at Merck and J&J. They delve into Mathai's innovative approach to creating transformative medicines, navigating the biotech industry, and the unique challenges of targeting 'undruggable' proteins. They also discuss the role of AI in drug discovery, the importance of strategic risk, and fostering team resilience and spirit in both large and small pharma companies. 00:00 Introduction and Guest Background00:56 Founding Thebans and Career Highlights01:43 Leadership at Merck and J&J02:18 Innovative Approaches at Parabilis Medicines04:09 Defining and Tackling Undruggable Targets09:27 Multivalent Drug Design and Bispecifics13:53 AI and Data Science in Drug Development19:28 Building and Leading World-Class Teams25:41 The Importance of Holding Conviction as an Entrepreneur26:25 Learning from Setbacks in the Biotech Industry30:10 Challenges and Innovations in Drug Development32:28 Navigating the Ups and Downs of the Biotech Industry36:02 The Mission and Future of Parabilis40:35 Personal Reflections and Advice for Entrepreneurs46:46 Book Recommendations and Closing Thoughts Don't forget to Like, Share, Subscribe, Rate, and Review! Keep up with Mathai Mammen;LinkedIn: https://www.linkedin.com/in/mathai-mammen/Website: https://parabilismed.com/ Follow Mike Rea On;Website: https://www.ideapharma.com/X: https://x.com/ideapharmaLinkedIn: https://www.linkedin.com/in/bigidea/ Listen to more fantastic podcast episodes: https://podcast.ideapharma.com/

Tired of spending money on data courses you never finish? Here are 7 essential books that will actually boost your analytical skills, with no subscription required! Plus, make sure to tune in till the end as one lucky listener will get a free book from this list! Get the books here!DISCLAIMER: Some of the links in this video are affiliate links, meaning if you click through and make a purchase, I may earn a commission at no extra cost to you.Storytelling with Data by Cole Nussbaumer Knaflic

AI - two little letters that we seemingly can't escape. AI is upending not only our professional lives but also our personal, everyday lives. 

With Artificial Intelligence (AI) becoming increasingly prominent in our everyday lives, discussions have begun on whether it should be taught in schools. In this podcast, we speak to Abhinav Dhall, Associate Professor in the Department of Data Science and AI at Monash University, to explore whether introducing AI in classrooms would prepare the next generation for the future or hinder their critical thinking skills.

Topics covered in this episode: * ty documentation site and uv migration guide* * uv build backend is now stable + other Astral news* * Refactoring long boolean expressions* * fastapi-ml-skeleton* Extras Joke Watch on YouTube About the show Sponsored by Sentry: pythonbytes.fm/sentry 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: ty documentation site and uv migration guide via Skyler Kasko Astral created a documentation site for ty (PR #744 in release 0.0.1-alpha.13). Astral added a page on migrating from pip to a uv project in the uv documentation. (PR #12382 in release 0.7.19). Talk Python episode on ty. Brian #2: uv build backend is now stable + other Astral news The uv build backend is now stable Tim Hopper via Python Developer Tooling Handbook From Charlie Marsh “The uv build backend is now stable, and considered ready for production use. An alternative to setuptools, hatchling, etc. for pure Python projects, with a focus on good defaults, user-friendly error messages, and performance. When used with uv, it's 10-35x faster.” “(In a future release, we'll make this the default.)” [build-system] requires = ["uv_build>=0.7.19,

From the viral article "Tech's Dumbest Mistake: Why Firing Programmers for AI Will Destroy Everything" on my newsletter at https://defragzone.substack.com/p/techs-dumbest-mistake-why-firing here are my thoughts about AI replacing programmers...    

Hollister Incorporated, a global leader in ostomy, continence, wound care and critical care products has announced an €80m R&D investment and a digital transformation project that will create approximately 50 new jobs in Ballina. The multimillion-euro investment aims to elevate Ballina into a global epicentre of expertise through novel device design and extensive site-wide training, setting a benchmark for digital transformation within Hollister's global network. This project is supported by the Irish Government through IDA Ireland. Minister for Social Protection and Minister for Rural and Community Development and the Gaeltacht Dara Calleary TD said: "This is a terrific day for Ballina and North Mayo with the announcement of 50 additional jobs for the region. Hollister is more than just a manufacturing plant in Ballina; it is one of the mainstays of our community. Today, second and third generations of families are employed there boosting the local economy and contributing to the everyday life of the town. I want to pay credit to Shane Caher and all of the staff in Hollister who have dedicated themselves to Ballina and to the West of Ireland but also to all of the past Hollister management and staff who's work, and commitment is the foundation of the plant's success today. Finally, I would like to acknowledge IDA Ireland for their continued support to Hollister and across Mayo. I very much look forward to what the next 50 years will bring for Hollister and Ballina" Minister of State at the Department of Enterprise, Tourism and Employment, Alan Dillon TD, said: "This €80 million investment by Hollister Inc. is very welcome news. It is a powerful endorsement of Ballina's skilled workforce and Ireland's reputation as a hub for innovation in healthcare manufacturing. To see a long-standing employer, like Hollister, continue to grow and evolve through cutting-edge research and digital transformation is very encouraging. The creation of 50 new high-quality jobs will also bring economic and social benefits to the region. On behalf of the Irish Government, I thank Hollister for its continued commitment to Ballina and the West of Ireland, and I wish the team there the very best for the future and many more years of success here in Co. Mayo." Founded in 1921 in Illinois, the US MedTech manufacturer has been part of the fabric of Ballina since 1976, where it now employs almost 1000 people. Hollister is currently recruiting in the areas of Engineering, Data Science and Business Services. To explore opportunities, visit Career Opportunities | Hollister IE. "We are thrilled to announce this significant milestone for Hollister Incorporated. Our commitment to innovation and excellence continues to drive us forward, and this investment in our research program and digital transformation project is a testament to the hard work and dedication of our entire team," said Shane Caher, Senior Director of Plant Operations and General Manager. "We look forward to the exciting opportunities that lie ahead as we continue to deliver on Our Mission to make life more rewarding and dignified for those who use our products and services." IDA Ireland CEO Michael Lohan said: "Since 1976, Hollister has been creating jobs and investment in Co. Mayo. In the intervening near 50 years, Hollister has again and again committed to and delivered on its ambitions for its Irish operations. Supporting Hollister and companies across IDA Ireland's client portfolio with R&D investment and digital transformation endeavours sits right at the heart of Adapt Intelligently: A Strategy for Sustainable Growth and Innovation 2025-29. I wish to congratulate Hollister and assure them of IDA Ireland's continued support." 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://...

Was passiert eigentlich, wenn modernste KI auf einen der geheimnisvollsten Arbeitgeber Deutschlands trifft?

This week Dr. Nadine Choueiter of Mount Sinai hosts a special episode of Pediheart: Pediatric Cardiology Today in which we speak with emeritus Professor of Pediatrics at the University of Toronto, Dr. Brian McCrindle about his career and life. How did he develop a love of pediatric cardiology? Who were some of his early mentors? How did he develop the international Kawasaki Disease Registry and how has he cultivated it despite minimal funding? How did he develop an interest in preventive cardiology? How can a young person make their clinical work also their academic work? Dr. McCrindle also shares some insights into navigating a successful life as well as retirement. This is a rare opportunity to be inspired by one of the great pediatric cardovascular researchers of the past 3 decades. 

Como é manter o motor de dados de uma dos maiores grupos de varejo do Brasil rodando sem parar — enquanto se experimenta tecnologias de IA Generativa que poucas empresas do mundo ousaram colocar em produção?Neste episódio especial, convidamos Lucas Eduardo Wichinevsky, Rodrigo Lucchesi e Marcelle Araujo Chiriboga Carvalho do Grupo Boticário, para abrir a caixa-preta da Engenharia de Machine Learning.Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas.Falamos no episódio:Marcelle Chiriboga - Gerente de Data Science de Lojas e Franquias no Grupo BoticárioLucas Eduardo Wichinevsky  - Gerente de Data Science de Tech Corporate  no Grupo BoticárioRodrigo Lucchesi -  Gerente de Data Science de Demanda e RGM no  no Grupo BoticárioNossa Bancada — Data Hackers:Monique Femme — Head of Community Management na Data HackersPaulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart

Do you like to dive into the details and intricacies of how Python executes and how we can optimize it? Well, do I have an episode for you. We welcome back Brandt Bucher to give us an update on the upcoming JIT compiler for Python and why it differs from JITs for languages such as C# and Java. Episode sponsors Posit Talk Python Courses Links from the show Brandt Bucher: github.com/brandtbucher PyCon Talk: What they don't tell you about building a JIT compiler for CPython: youtube.com Specializing, Adaptive Interpreter Episode: talkpython.fm Watch this episode on YouTube: youtube.com Episode #512 deep-dive: talkpython.fm/512 Episode transcripts: talkpython.fm --- 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

Andrew Ackerman is the Head of REACH Labs at Second Century Ventures, the strategic venture arm of the National Association of Realtors® (NAR). Backed by NAR, SCV invests in early-stage PropTech and construction tech companies, providing them with access to a vast network of real estate professionals and industry expertise. A former entrepreneur, angel investor, and accelerator director at Dreamit Ventures, Andrew has backed 70+ startups and designed structured programs to help founders raise capital and close deals faster. He is also the author of “The Entrepreneur's Odyssey,” a story-driven startup guide, and a frequent contributor to Forbes, Propmodo, and other leading publications.(01:19) – Andrew Ackerman's Journey in PropTech (03:06) – Evolution of the PropTech Landscape (06:40) – The Role of Reach Labs, Second Century Ventures and NAR (10:06) – Challenges in Real Estate Transactions (13:00) – Venture Returns in PropTech (21:01) – 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. (23:03) – Qualifying Investment Opportunities (23:32) – Challenges in Portfolio Construction & Valuation Dilemmas (30:00) – The Role of Venture Debt (35:59) – The Entrepreneur's Odyssey(29:22) - Collaboration Superpower: Richard Nixon

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Huzaifa Sayed, Senior Manager of Data Science at The Home Depot:What are some business operations solvable via AI?How can AI technologies help streamline, improve, or otherwise support operations?What does the future hold for leveraging AI as part of operational activities?What final piece of advice would you give to someone trying to find ways to leverage AI technology used to support business functions?Huzaifa Sayed, Senior Manager of Data Science at The Home DepotFollow Huzaifa Sayed

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Bhavna Mehta , Assistant Vice President at Cincinnati Children's Hospital Medical Center: What are some examples of ways AI technology can support healthcare operations and decision making?Describe some of the challenges specific to healthcare when thinking about leveraging AI.How do you think AI will work with us in the future to support and improve community health?Bhavna Mehta, Senior Manager of Data Science at The Home DepotFollow Bhavna Mehta

Despite unprecedented data abundance and widespread data science education, even experienced data professionals still struggle to interpret data effectively. They draw wrong conclusions, miss critical insights, or fail to communicate findings in actionable ways.In this episode, Nicholas Kelly joins Dr. Genevieve Hayes to tackle the critical challenge of data interpretation - revealing why technical expertise alone isn't enough and sharing practical frameworks for transforming raw data into actionable business insights that drive real organisational change.This conversation reveals:The four primary challenges that make data interpretation so difficult [02:24]Why ChatGPT and AI tools are changing the data interpretation landscape [06:23]The "Five Whys" technique that ensures you're asking the right questions instead of wasting time on problems everyone already understands [17:32]Why successful data projects don't end with presenting insights and what to do next [20:01]Guest BioNicholas Kelly is the founder of Delivering Data Analytics, a consultancy focused on helping organisations enable their teams to make smarter, faster, and more confident decisions through data and AI. He is also the author of Delivering Data Analytics and the recently released How to Interpret Data.LinksNicholas's WebsiteConnect with Nicholas on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Join host Tiana Zhao, FSA, CERA, as she explores the emerging field of marketing actuaries in this insightful episode of the SOA Marketing and Distribution podcast. Discover how these professionals blend actuarial science with marketing strategies to drive data-informed business decisions. Tiana breaks down the key responsibilities of marketing actuaries, including data analysis, predictive modeling, customer segmentation, and pricing optimization. Learn how this evolving role is shaping the future of marketing in our increasingly data-driven business landscape. Whether you're an actuary looking to expand your skillset or a marketing professional curious about the power of actuarial science, this episode offers valuable insights into this exciting intersection of disciplines.

Most LLM-powered features do not break at the model. They break at the context. So how do you retrieve the right information to get useful results, even under vague or messy user queries? In this episode, we hear from Eric Ma, who leads data science research in the Data Science and AI group at Moderna. He shares what it takes to move beyond toy demos and ship LLM features that actually help people do their jobs. We cover: • How to align retrieval with user intent and why cosine similarity is not the answer • How a dumb YAML-based system outperformed so-called smart retrieval pipelines • Why vague queries like “what is this all about” expose real weaknesses in most systems • When vibe checks are enough and when formal evaluation is worth the effort • How retrieval workflows can evolve alongside your product and user needs If you are building LLM-powered systems and care about how they work, not just whether they work, this one is for you. LINKS Eric's website (https://ericmjl.github.io/) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Hugo's recent newsletter about upcoming events and more! (https://hugobowne.substack.com/p/stop-building-agents)

Andrew Parnell, Professor of Data Science for Climate at UCD, discusses Europe's extreme heatwave.

Topics covered in this episode: * Python Cheat Sheets from Trey Hunner* * Automatisch* * mureq-typed* * My CLI World* Extras Joke Watch on YouTube About the show Sponsored by Posit: pythonbytes.fm/connect 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: Python Cheat Sheets from Trey Hunner Some fun sheets Python f-string tips & cheat sheets Python's pathlib module Python's many command-line utilities Michael #2: Automatisch Open source Zapier alternative Automatisch helps you to automate your business processes without coding. Use their affordable cloud solution or self-host on your own servers. Automatisch allows you to store your data on your own servers, good for companies dealing with sensitive user data, particularly in industries like healthcare and finance, or those based in Europe bound by General Data Protection Regulation (GDPR). Michael #3: mureq-typed Single file, zero-dependency alternative to requests. Fully typed. Modern Python tooling. Typed version of mureq (covered in 2022 on episode 268) Intended to be vendored in-tree by Linux systems software and other lightweight applications. mureq-typed is a drop-in, fully API compatible replacement for mureq updated with modern Python tooling: Type checked with mypy, ty, and pyrefly. Formatted with black, no ignore rules necessary. Linted with ruff (add these rules for mureq.py to your per-file-ignores). Brian #4: My CLI World Frank Wiles Encouragement to modify your command line environment Some of Franks tools direnv, zoxide, fd, ack, atuin, just Also some aliases, like gitpulllog Notes We covered poethepoet recently, if just just isn't cutting it for you. I tried to ilke starship, bit for some reason with my setup, it slows down the shell too much. Extras Brian: Interesting read of the week: New theory proposes time has three dimensions, with space as a secondary effect Michael's: New quantum theory of gravity brings long-sought 'theory of everything' a crucial step closer Joke: Brian read a few quotes from the book Disappointing Affirmations, by Dave Tarnowski “You are always just a moment away from your next worst day ever. Or your next best day ever, but let's be realistic.” “You can be anything you want. And yet you keep choosing to be you. I admire your dedication to the role.” “Today I am letting go of the things that are holding me back from the life that I want to live. Then I'm picking them all up again because I have separation anxiety.”

Andreas Voniatis, founder of Arteos and author of "Data-Driven SEO with Python," is revolutionizing the SEO landscape by integrating data science and AI into organic growth strategies. He emphasizes the need for businesses, particularly in the B2B and technology sectors, to move beyond traditional SEO practices that often rely on guesswork. Instead, Fanatis advocates for a math-driven approach that leverages data to ensure that companies can achieve exponential growth in their online visibility and traffic.As the conversation unfolds, Voniatis discusses the significant shift in user behavior from traditional search engines, which typically present a list of links, to AI-driven interactions that provide summarized answers. This evolution poses a challenge for businesses, as they must adapt to a landscape where AI can solve problems directly, potentially bypassing the need for human expertise. Voniatis argues that to remain relevant, companies must prepare for a future where AI not only recommends content but also understands the nuances of their offerings.The discussion also highlights the importance of creating unique, data-rich content that stands out in an increasingly crowded digital space. Voniatis explains that simply producing high-quality content is no longer sufficient; businesses must ensure their content correlates with AI's understanding of truth and relevance. By focusing on proprietary insights and addressing the specific needs of target audiences, companies can differentiate themselves from competitors who rely on generic SEO strategies.Finally, Voniatis outlines key metrics for measuring success in this new SEO paradigm. He emphasizes the importance of tracking both traffic sources and brand searches to gauge the effectiveness of SEO efforts. By blending data science with creative content strategies, businesses can not only improve their search rankings but also enhance their overall brand visibility and engagement in the digital marketplace. All our Sponsors: https://businessof.tech/sponsors/ Do you want the show on your podcast app or the written versions of the stories? Subscribe to the Business of Tech: https://www.businessof.tech/subscribe/Looking for a link from the stories? The entire script of the show, with links to articles, are posted in each story on https://www.businessof.tech/ Support the show on Patreon: https://patreon.com/mspradio/ Want to be a guest on Business of Tech: Daily 10-Minute IT Services Insights? Send Dave Sobel a message on PodMatch, here: https://www.podmatch.com/hostdetailpreview/businessoftech Want our stuff? Cool Merch? Wear “Why Do We Care?” - Visit https://mspradio.myspreadshop.com Follow us on:LinkedIn: https://www.linkedin.com/company/28908079/YouTube: https://youtube.com/mspradio/Facebook: https://www.facebook.com/mspradionews/Instagram: https://www.instagram.com/mspradio/TikTok: https://www.tiktok.com/@businessoftechBluesky: https://bsky.app/profile/businessof.tech

Emily Riederer is a Data Science Senior Manager at Credit Risk Modeling Capital One. Her website can be found here: https://www.emilyriederer.com/   Follow along on Bluesky: Emily: ‪@emilyriederer.bsky.social‬ Ellie: @epiellie.bsky.social Lucy: @lucystats.bsky.social  

What does it take to actually ship LLM-powered features, and what breaks when you connect them to real production data? In this episode, we hear from Philip Carter — then a Principal PM at Honeycomb and now a Product Management Director at Salesforce. In early 2023, he helped build one of the first LLM-powered SaaS features to ship to real users. More recently, he and his team built a production-ready MCP server. We cover: • How to evaluate LLM systems using human-aligned judges • The spreadsheet-driven process behind shipping Honeycomb's first LLM feature • The challenges of tool usage, prompt templates, and flaky model behavior • Where MCP shows promise, and where it breaks in the real world If you're working on LLMs in production, this one's for you! LINKS So We Shipped an AI Product: Did it Work? by Philip Carter (https://www.honeycomb.io/blog/we-shipped-ai-product) Vanishing Gradients YouTube Channel (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Hugo's recent newsletter about upcoming events and more! (https://hugobowne.substack.com/p/ai-as-a-civilizational-technology)

If you're doing data science and have mostly spent your time doing exploratory or just local development, this could be the episode for you. We are joined by Catherine Nelson to discuss techniques and tools to move your data science game from local notebooks to full-on production workflows. Episode sponsors Agntcy Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show New Course: LLM Building Blocks for Python: training.talkpython.fm Catherine Nelson LinkedIn Profile: linkedin.com Catherine Nelson Bluesky Profile: bsky.app Enter to win the book: forms.google.com Going From Notebooks to Scalable Systems - PyCon US 2025: us.pycon.org Going From Notebooks to Scalable Systems - Catherine Nelson – YouTube: youtube.com From Notebooks to Scalable Systems Code Repository: github.com Building Machine Learning Pipelines Book: oreilly.com Software Engineering for Data Scientists Book: oreilly.com Jupytext - Jupyter Notebooks as Markdown Documents: github.com Jupyter nbconvert - Notebook Conversion Tool: github.com Awesome MLOps - Curated List: github.com Watch this episode on YouTube: youtube.com Episode #511 deep-dive: talkpython.fm/511 Episode transcripts: talkpython.fm --- 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

Can you clearly articulate what makes your data science work valuable - both to yourself and to your key stakeholders? Without this clarity, you'll struggle to stay focused and convince others of your worth.In this Value Boost episode, Dr. Peter Prevos joins Dr. Genevieve Hayes to share how creating a compelling value proposition transformed his data team from report writers to strategic partners by providing both external credibility and internal direction.This episode reveals:Why a clear purpose statement serves as both an external marketing tool and an internal compass for daily decision-making [02:09]A framework for identifying your stakeholders' true pain points and how your data skills can address them [04:48]A practical first step to develop your own value statement that aligns with organizational strategy while focusing your daily work [06:53]Guest BioDr Peter Prevos is a water engineer and manages the data science function at a water utility in regional Victoria. He runs leading courses in data science for water professionals, holds an MBA and a PhD in business, and is the author of numerous books about data science and magic.LinksConnect with Peter on LinkedInA Brief Guide to Providing Insights as a Service (IaaS)Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Adatépítész -az első magyar datapodcast Minden ami hír, érdekesség, esemény vagy tudásmorzsa az  adat, datascience, adatbányászat és hasonló kockaságok világából. Become a Patron! Pör GI Joe a nörd FB kiteregeti az intim kérdéseid

This week we're looking at AI and public services. How far could AI tools help to tackle stagnant public sector productivity? What dangers are associated with AI adoption? And how can these dangers be addressed? Artificial intelligence is increasingly being touted as a game-changer across various sectors, including public services. But while AI presents significant opportunities for improving efficiency and effectiveness, concerns about fairness, equity, and past failures in public sector IT transformations loom large. And, of course, the idea of tech moguls like Elon Musk wielding immense influence over our daily lives is unsettling for many. So, what are the real opportunities AI offers for public services? What risks need to be managed? And how well are governments—particularly in the UK—rising to the challenge? In this episode, we dive into these questions with three expert guests who have recently published an article in The Political Quarterly on the subject: Helen Margetts – Professor of Society and the Internet at the Oxford Internet Institute, University of Oxford, and Director of the Public Policy Programme at The Alan Turing Institute. Previously, she was Director of the School of Public Policy at UCL. Cosmina Dorobantu – Co-director of the Public Policy Programme at The Alan Turing Institute. Jonathan Bright – Head of Public Services and AI Safety at The Alan Turing Institute. Mentioned in this episode: Margetts, H., Dorobantu, C. and Bright, J. (2024), How to Build Progressive Public Services with Data Science and Artificial Intelligence. The Political Quarterly. Transcription link: https://uncoveringpolitics.com/episodes/ai-and-public-services/transcript Date of episode recording: 2025-02-13T00:00:00Z Duration: 00:42:48 Language of episode: English (UK) TAGS: AI, government, politics, bureaucracy, political quarterly, efficiency Presenter:Alan Renwick Guests: Helen Margettes, Cosmina Dorobantu, Jonathan Bright Producer: Eleanor Kingwell-Banham

Key Takeaways for local government's data and software:Be intentional during idea stage with disciplined winnowing.The difference between a platform and a tool - and what will best serve your needs.Sunk cost fallacy and resisting the urge to double down.Featured Guest:Raman Shaw – Owner, Raman Shah Data ScienceConnect with Raman on LinkedIn Voices in Local Government Podcast HostsJoe Supervielle and Angelica WedellResourcesLearn more from Raman in his three-part blog series, Software Trouble.ICMA Annual Conference, October 25-29 in Tampa.  

Topics covered in this episode: * The Python Language Summit 2025* Fixing Python Properties * complexipy* * juvio* Extras Joke Watch on YouTube About the show Sponsored by Posit: pythonbytes.fm/connect 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: The Python Language Summit 2025 Write up by Seth Michael Larson How can we make breaking changes less painful?: talk by Itamar Oren An Uncontentious Talk about Contention: talk by Mark Shannon State of Free-Threaded Python: talk by Matt Page Fearless Concurrency: talk by Matthew Parkinson, Tobias Wrigstad, and Fridtjof Stoldt Challenges of the Steering Council: talk by Eric Snow Updates from the Python Docs Editorial Board: talk by Mariatta PEP 772 - Packaging Governance Process: talk by Barry Warsaw and Pradyun Gedam Python on Mobile - Next Steps: talk by Russell Keith-Magee What do Python core developers want from Rust?: talk by David Hewitt Upstreaming the Pyodide JS FFI: talk by Hood Chatham Lightning Talks: talks by Martin DeMello, Mark Shannon, Noah Kim, Gregory Smith, Guido van Rossum, Pablo Galindo Salgado, and Lysandros Nikolaou Brian #2: Fixing Python Properties Will McGugan “Python properties work well with type checkers such Mypy and friends. … The type of your property is taken from the getter only. Even if your setter accepts different types, the type checker will complain on assignment.” Will describes a way to get around this and make type checkers happy. He replaces @property with a descriptor. It's a cool technique. I also like the way Will is allowing different ways to use a property such that it's more convenient for the user. This is a cool deverloper usability trick. Brian #3: complexipy Calculates the cognitive complexity of Python files, written in Rust. Based on the cognitive complexity measurement described in a white paper by Sonar Cognitive complexity builds on the idea of cyclomatic complexity. Cyclomatic complexity was intended to measure the “testability and maintainability” of the control flow of a module. Sonar argues that it's fine for testability, but doesn't do well with measuring the “maintainability” part. So they came up with a new measure. Cognitive complexity is intended to reflects the relative difficulty of understanding, and therefore of maintaining methods, classes, and applications. complexipy essentially does that, but also has a really nice color output. Note: at the very least, you should be using “cyclomatic complexity” try with ruff check --select C901 But also try complexipy. Great for understanding which functions might be ripe for refactoring, adding more documentation, surrounding with more tests, etc. Michael #4: juvio uv kernel for Jupyter ⚙️ Automatic Environment Setup: When the notebook is opened, Juvio installs the dependencies automatically in an ephemeral virtual environment (using uv), ensuring that the notebook runs with the correct versions of the packages and Python

Remora, founded in 2020 in Michigan, retrofits diesel locomotives and semi-trucks with onboard systems that capture, liquify, and purify up to 90% of CO₂ emissions – while also cutting soot, particulates, and NOₓ – then stores it for offloading and sale, sharing revenue with operators. Backed by $117 M in venture funding and piloting with major players like Union Pacific, Norfolk Southern, Ryder, Werner, and DHL, the company overcame early design issues (like waterlogging and backpressure) and is now scaling for broader deployment.–Paul is the co-founder and CEO at Remora. In 2021, he was named to the Forbes 30 Under 30 list. He earned his bachelor's degree in Statistics and Data Science from Yale University, where his research received the Porter Prize, the highest class-wide thesis prize.–In this podcast, we talked about how they got investors excited to invest $117M, why unit economics are so important, the joy of doing hard things, why expertise is overrated, the reason why reading biographies is so helpful, how he got the idea for this company as a senior at Yale University, why you should watch the movies Tommy Boy and Braveheart, and how the cost of capital influenced his choice of business model.–

Today on Elixir Wizards, hosts Sundi Myint and Charles Suggs catch up with Sean Moriarity, co-creator of the Nx project and author of Machine Learning in Elixir. Sean reflects on his transition from the military to a civilian job building large language models (LLMs) for software. He explains how the Elixir ML landscape has evolved since the rise of ChatGPT, shifting from building native model implementations toward orchestrating best-in-class tools. We discuss the pragmatics of adding ML to Elixir apps: when to start with out-of-the-box LLMs vs. rolling your own, how to hook into Python-based libraries, and how to tap Elixir's distributed computing for scalable workloads. Sean closes with advice for developers embarking on Elixir ML projects, from picking motivating use cases to experimenting with domain-specific languages for AI-driven workflows. Key topics discussed in this episode: The evolution of the Nx (Numerical Elixir) project and what's new with ML in Elixir Treating Elixir as an orchestration layer for external ML tools When to rely on off-the-shelf LLMs vs. custom models Strategies for integrating Elixir with Python-based ML libraries Leveraging Elixir's distributed computing strengths for ML tasks Starting ML projects with existing data considerations Synthetic data generation using large language models Exploring DSLs to streamline AI-powered business logic Balancing custom frameworks and service-based approaches in production Pragmatic advice for getting started with ML in Elixir Links mentioned: https://hexdocs.pm/nx/intro-to-nx.html https://pragprog.com/titles/smelixir/machine-learning-in-elixir/ https://magic.dev/ https://smartlogic.io/podcast/elixir-wizards/s10-e10-sean-moriarity-machine-learning-elixir/ Pragmatic Bookshelf: https://pragprog.com/ ONNX Runtime Bindings for Elixir: https://github.com/elixir-nx/ortex https://github.com/elixir-nx/bumblebee Silero Voice Activity Detector: https://github.com/snakers4/silero-vad Paulo Valente Graph Splitting Article: https://dockyard.com/blog/2024/11/06/2024/nx-sharding-update-part-1 Thomas Millar's Twitter https://x.com/thmsmlr https://github.com/thmsmlr/instructorex https://phoenix.new/ https://tidewave.ai/ https://en.wikipedia.org/wiki/BERT(language_model) Talk: PyTorch: Fast Differentiable Dynamic Graphs in Python (https://www.youtube.com/watch?v=am895oU6mmY) by Soumith Chintala https://hexdocs.pm/axon/Axon.html https://hexdocs.pm/exla/EXLA.html VLM (Vision Language Models Explained): https://huggingface.co/blog/vlms https://github.com/ggml-org/llama.cpp Vector Search in Elixir: https://github.com/elixir-nx/hnswlib https://www.amplified.ai/ Llama 4 https://mistral.ai/ Mistral Open-Source LLMs: https://mistral.ai/ https://github.com/openai/whisper Elixir Wizards Season 5: Adopting Elixir https://smartlogic.io/podcast/elixir-wizards/season-five https://docs.ray.io/en/latest/ray-overview/index.html https://hexdocs.pm/flame/FLAME.html https://firecracker-microvm.github.io/ https://fly.io/ https://kubernetes.io/ WireGuard VPNs https://www.wireguard.com/ https://hexdocs.pm/phoenixpubsub/Phoenix.PubSub.html https://www.manning.com/books/deep-learning-with-python Code BEAM 2025 Keynote: Designing LLM Native Systems - Sean Moriarity Ash Framework https://ash-hq.org/ Sean's Twitter: https://x.com/seanmoriarity Sean's Personal Blog: https://seanmoriarity.com/ Erlang Ecosystems Foundation Slack: https://erlef.org/slack-invite/erlef Elixir Forum https://elixirforum.com/ Sean's LinkedIn: https://www.linkedin.com/in/sean-m-ba231a149/ Special Guest: Sean Moriarity.

Are you using Polars for your data science work? Maybe you've been sticking with the tried-and-true Pandas? There are many benefits to Polars directly of course. But you might not be aware of all the excellent tools and libraries that make Polars even better. Examples include Patito which combines Pydantic and Polars for data validation and polars_encryption which adds AES encryption to selected columns. We have Christopher Trudeau back on Talk Python To Me to tell us about his list of excellent libraries to power up your Polars game and we also talk a bit about his new Polars course. Episode sponsors Agntcy Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show New Theme Song (Full-Length Download and backstory): talkpython.fm/blog Polars for Power Users Course: training.talkpython.fm Awesome Polars: github.com Polars Visualization with Plotly: docs.pola.rs Dataframely: github.com Patito: github.com polars_iptools: github.com polars-fuzzy-match: github.com Nucleo Fuzzy Matcher: github.com polars-strsim: github.com polars_encryption: github.com polars-xdt: github.com polars_ols: github.com Least Mean Squares Filter in Signal Processing: www.geeksforgeeks.org polars-pairing: github.com Pairing Function: en.wikipedia.org polars_list_utils: github.com Harley Schema Helpers: tomburdge.github.io Marimo Reactive Notebooks Episode: talkpython.fm Marimo: marimo.io Ahoy Narwhals Podcast Episode Links: talkpython.fm Watch this episode on YouTube: youtube.com Episode #510 deep-dive: talkpython.fm/510 Episode transcripts: talkpython.fm --- 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

Once the stuff of science fiction, deepfake technology has rapidly become one of the most powerful—and consequential—applications of generative AI, blurring the line between reality and illusion and reshaping how we trust what we see and hear online. This month we delve into this phenomenon with Professor Hany Farid, a pioneer in digital forensics, and  Professor Siwei Lyu, whose lab develops state-of-the-art deepfake detection methods.Together, they'll walk us through the data journey—from the vast raw data sets that fuel synthetic media to the pixel-level signatures that can unmask it. Whether you're a computer scientist, policymaker, or simply curious about how synthetic content is transforming our information landscape, join us for an in-depth conversation about turning data into both convincing illusions and robust defenses—and learn how we can preserve trust and truth in our rapidly evolving digital world.   Our guests: Hany Farid is a professor at the University of California, Berkeley, with a joint appointment in the Department of Electrical Engineering and Computer Sciences and the School of Information. He is also a member of the Berkeley Artificial Research Intelligence Lab, Berkeley Institute for Data Science, Center for Innovation in Vision and Optics, Development Engineering program, Vision Science program, and is a senior faculty advisor for the Center for Long-Term Cybersecurity. Siwei Lyu is a SUNY Distinguished Professor and a SUNY Empire Innovation Professor at the Department of Computer Science and Engineering, the director of the UB Media Forensic Lab, and founding co-director of the Center for Information Integrity at the University of Buffalo, State University of New York.        

Carly Brantner is an assistant professor of Biostatistics & Bioinformatics at Duke University and Duke Clinical Research Institute. Resources from this episode: multicate: R package for estimating conditional average treatment effects across one or more studies using machine learning methods PCORnet® Front Door: Access point for potential investigators, patient groups, and other stakeholders to connect with PCORnet and get support for potential research studies Patient-Centered Outcomes Data Repository (PDOCR): De-identified data from 24 (and counting) PCORI-funded studies Follow along on Bluesky: Carly: @carlybrantner.bsky.social Ellie: @epiellie.bsky.social Lucy: @lucystats.bsky.social  

Today's guest is Yunke Xiang, Global Head of Data Science for Manufacturing, Supply Chain, and Quality at Sanofi. Yunke joins Emerj Editorial Director Matthew DeMello to discuss the challenges that slow AI adoption in life sciences manufacturing, highlighting how fragmented data systems and legacy infrastructure create hurdles for AI initiatives. In this episode, Yunke explains how years of acquisitions and siloed data have made building a cohesive data foundation difficult, impacting AI's potential in manufacturing and supply chain optimization. Yunke shares Sanofi's approach to balancing build versus buy decisions for AI solutions and the critical role leadership plays in fostering an environment where data science can thrive. Yunke also reflects on the evolving landscape of AI in pharma manufacturing and the importance of strong governance and collaboration for successful implementation. 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! Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1.

Topics covered in this episode: * Free-threaded Python no longer “experimental” as of Python 3.14* typed-ffmpeg pyleak * Optimizing Test Execution: Running live_server Tests Last with pytest* Extras Joke Watch on YouTube About the show Sponsored by PropelAuth: pythonbytes.fm/propelauth66 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: Free-threaded Python no longer “experimental” as of Python 3.14 “PEP 779 ("Criteria for supported status for free-threaded Python") has been accepted, which means free-threaded Python is now a supported build!” - Hugo van Kemenade PEP 779 – Criteria for supported status for free-threaded Python As noted in the discussion of PEP 779, “The Steering Council (SC) approves PEP 779, with the effect of removing the “experimental” tag from the free-threaded build of Python 3.14.” We are in Phase II then. “We are confident that the project is on the right path, and we appreciate the continued dedication from everyone working to make free-threading ready for broader adoption across the Python community.” “Keep in mind that any decision to transition to Phase III, with free-threading as the default or sole build of Python is still undecided, and dependent on many factors both within CPython itself and the community. We leave that decision for the future.” How long will all this take? According to Thomas Wouters, a few years, at least: “In other words: it'll be a few years at least. It can't happen before 3.16 (because we won't have Stable ABI support until 15) and may well take longer.” Michael #2: typed-ffmpeg typed-ffmpeg offers a modern, Pythonic interface to FFmpeg, providing extensive support for complex filters with detailed typing and documentation. Inspired by ffmpeg-python, this package enhances functionality by addressing common limitations, such as lack of IDE integration and comprehensive typing, while also introducing new features like JSON serialization of filter graphs and automatic FFmpeg validation. Features : Zero Dependencies: Built purely with the Python standard library, ensuring maximum compatibility and security. User-Friendly: Simplifies the construction of filter graphs with an intuitive Pythonic interface. Comprehensive FFmpeg Filter Support: Out-of-the-box support for most FFmpeg filters, with IDE auto-completion. Integrated Documentation: In-line docstrings provide immediate reference for filter usage, reducing the need to consult external documentation. Robust Typing: Offers static and dynamic type checking, enhancing code reliability and development experience. Filter Graph Serialization: Enables saving and reloading of filter graphs in JSON format for ease of use and repeatability. Graph Visualization: Leverages graphviz for visual representation, aiding in understanding and debugging. Validation and Auto-correction: Assists in identifying and fixing errors within filter graphs. Input and Output Options Support: Provide a more comprehensive interface for input and output options, including support for additional codecs and formats. Partial Evaluation: Enhance the flexibility of filter graphs by enabling partial evaluation, allowing for modular construction and reuse. Media File Analysis: Built-in support for analyzing media files using FFmpeg's ffprobe utility, providing detailed metadata extraction with both dictionary and dataclass interfaces. Michael #3: pyleak Detect leaked asyncio tasks, threads, and event loop blocking with stack trace in Python. Inspired by goleak. Use as context managers or function dectorators When using no_task_leaks, you get detailed stack trace information showing exactly where leaked tasks are executing and where they were created. Even has great examples and a pytest plugin. Brian #4: Optimizing Test Execution: Running live_server Tests Last with pytest Tim Kamanin “When working with Django applications, it's common to have a mix of fast unit tests and slower end-to-end (E2E) tests that use pytest's live_server fixture and browser automation tools like Playwright or Selenium. ” Tim is running E2E tests last for Faster feedback from quick tests To not tie up resources early in the test suite. He did this with custom “e2e” marker Implementing a pytest_collection_modifyitems hook function to look for tests using the live_server fixture, and for them automatically add the e2e marker to those tests move those tests to the end The reason for the marker is to be able to Just run e2e tests with -m e2e Avoid running them sometimes with -m "not e2e" Cool small writeup. The technique works for any system that has some tests that are slower or resource bound based on a particular fixture or set of fixtures. Extras Brian: Is Free-Threading Our Only Option? - Interesting discussion started by Eric Snow and recommended by John Hagen Free-threaded Python on GitHub Actions - How to add FT tests to your projects, by Hugo van Kemenade Michael: New course! LLM Building Blocks in Python Talk Python Deep Dives Complete: 600K Words of Talk Python Insights .folders on Linux Write up on XDG for Python devs. They keep pulling me back - ChatGPT Pro with o3-pro Python Bytes is the #1 Python news podcast and #17 of all tech news podcasts. Python 3.13.4, 3.12.11, 3.11.13, 3.10.18 and 3.9.23 are now available Python 3.13.5 is now available! Joke: Naming is hard

If you're looking to leverage the insane power of modern GPUs for data science and ML, you might think you'll need to use some low-level programming language such as C++. But the folks over at NVIDIA have been hard at work building Python SDKs which provide nearly native level of performance when doing Pythonic GPU programming. Bryce Adelstein Lelbach is here to tell us about programming your GPU in pure Python. Episode sponsors Posit Agntcy Talk Python Courses Links from the show Bryce Adelstein Lelbach on Twitter: @blelbach Episode Deep Dive write up: talkpython.fm/blog NVIDIA CUDA Python API: github.com Numba (JIT Compiler for Python): numba.pydata.org Applied Data Science Podcast: adspthepodcast.com NVIDIA Accelerated Computing Hub: github.com NVIDIA CUDA Python Math API Documentation: docs.nvidia.com CUDA Cooperative Groups (CCCL): nvidia.github.io Numba CUDA User Guide: nvidia.github.io CUDA Python Core API: nvidia.github.io Numba (JIT Compiler for Python): numba.pydata.org NVIDIA's First Desktop AI PC ($3,000): arstechnica.com Google Colab: colab.research.google.com Compiler Explorer (“Godbolt”): godbolt.org CuPy: github.com RAPIDS User Guide: docs.rapids.ai Watch this episode on YouTube: youtube.com Episode #509 deep-dive: talkpython.fm/509 Episode transcripts: talkpython.fm --- 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