Podcasts about Data science

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)

In der aktuellen Podcast-Folge spricht Dr. Nicolas Conze, Head of Data Science bei docport, über die digitale Transformation im Gesundheitswesen – insbesondere in der hausärztlichen Versorgung. Für Conze liegt der Schlüssel zu einer modernen, patientenzentrierten Medizin in der intelligenten Nutzung und Strukturierung von Gesundheitsdaten. „Nicht KI ist die Basis, sondern saubere, semantisch strukturierte Daten“, betont er.

Our guest today is Dawn Choo, founder of Interview Master and ex Data Scientist from Amazon and Meta. In our conversation, we first dive into Dawn's past Data Science projects at Amazon and Instagram. She explains how a pet project skyrocketed her career at Amazon and also shares details on the most impactful project that she worked on at Instagram. We then discuss Dawn's experience living in a van for a year before digging into Interview Master: a platform to help Data Scientists and Data Analysts land their dream job while leveraging AI agents to provide instant feedback! If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.

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

In this episode of AI + a16z, dbt Labs founder and CEO Tristan Handy sits down with a16z's Jennifer Li and Matt Bornstein to explore the next chapter of data engineering — from the rise (and plateau) of the modern data stack to the growing role of AI in analytics and data engineering. As they sum up the impact of AI on data workflows: The interesting question here is human-in-the-loop versus human-not-in-the-loop. AI isn't about replacing analysts — it's about enabling self-service across the company. But without a human to verify the result, that's a very scary thing.Among other specific topics, they also discuss how automation and tooling like SQL compilers are reshaping how engineers work with data; dbt's new Fusion Engine and what it means for developer workflows; and what to make of the spate of recent data-industry acquisitions and ambitious product launches.Follow everyone on X:Tristan HandyJennifer LiMatt Bornstein Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

361Firm's Menlo Park Conference - Secondaries Panel led by Anurag ChandraSUMMARY: The 361Firm's Menlo Park Secondaries Panel discussed the evolving venture secondary market, emphasizing its growth and challenges. Key points included the extended liquidity cycles, now averaging 14 years, and the structural issues preventing companies from going public earlier. Panelists highlighted the role of secondary funds in addressing liquidity needs, particularly for smaller funds and employees. They noted the importance of data science in identifying high-potential companies and the need for realistic valuations. The discussion also covered the complexities of secondary transactions, including the impact of SPACs and the potential for tax credits through donor-advised funds.KEYWORDSVenture secondary market, liquidity cycles, private asset classes, practical venture capital, AI impact, technology sector, secondary funds, LP distribution, exit strategies, valuation challenges, data science, late-stage investments, employee liquidity, secondary market growth, investment risks.SPEAKERSAnurag Chandra, Dave McClure (Practical VC, 500 Startups), Raj Gollamudi (One Prime Capital), Lara Druyan (SV Data Capital, Palo Alto), Paul Kang (SFO), Eli Tenenbaum (SFO), Mark Sanor (361Firm), Reg Athwal You can subscribe to various 361 events and content at https://361firm.com/subs. For reference: Web: www.361firm.com/homeOnboard as Investor: https://361.pub/shortdiagOnboard Deals 361: www.361firm.com/onbOnboard as Banker: www.361firm.com/bankersEvents: www.361firm.com/eventsContent: www.youtube.com/361firmWeekly Digests: www.361firm.com/digest

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.        

In this episode of Data Science at Home, we explore the fascinating world of neuromorphic computing — a brain-inspired approach to computation that could reshape the future of AI and robotics. The episode breaks down how neuromorphic systems differ from conventional AI architectures like transformers and LLMs, diving into spiking neural networks (SNNs), their benefits in energy efficiency and real-time processing, and their limitations in training and scalability. Real-world applications are highlighted, including low-power drones, hearing aids, and event-based cameras. Francesco closes with a vision of hybrid systems where neuromorphic chips and LLMs coexist, blending biological inspiration with modern AI.

Internal data science teams face a unique challenge - they're providing an invisible service that only gets noticed when something goes wrong. This puts data scientists in the awkward position of having to market themselves within their own organization, without any marketing training.In this episode, Dr. Peter Prevos joins Dr. Genevieve Hayes to share how he applied his PhD research in services marketing to transform his water utility's data team from "report writers" to strategic partners by positioning data science as "Insights-as-a-Service."This episode explains:Why treating data science as "Customer Satisfaction Engineering" rather than technical implementation shifts everything about team effectiveness [08:19]How understanding both the financial and psychological "price" users pay for insights leads to dramatically better adoption [14:36]The treasure hunt technique that transformed how stakeholders discover and engage with available data resources [18:17]Why the mantra "99% of business problems don't need machine learning" can paradoxically increase your data science impact [22:29]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

Welcome back to the Alt Goes Mainstream podcast.Today's episode is with someone who is running the long race — in investing and in running.We sit down with prolific long distance runner, Blue Owl Capital's Ivan Zinn, who has been a pioneer in alternative credit and asset-based finance.Ivan has had a long career in private credit. He started at DLJ before joining Leonard Green & Partners and Highbridge Capital. He then joined HBK before founding pioneering private credit firm Atalaya Capital Management, where he was also the CIO. Ivan and team grew Atalaya to over $10B in AUM from 2006 to 2024 before being acquired by Blue Owl Capital for $450M (and $800M with earnouts).As part of the transaction, Ivan became Managing Director at Blue Owl and is the Head of Alternative Credit, where the firm is now expanding its footprint due to Atalaya's expertise. Ivan is as prolific outside of the office as he is in it — he is a long distance runner, running 100 mile races, and was a NCAA All-American tennis player, which comes as no surprise given the discipline, focus and expertise required to excel at the activities he's done throughout his career in work and sport. He's also a Board member of the USTA Foundation.Ivan and I had a fascinating conversation about the evolution of private credit and the growth of asset-based finance. We discussed:How and why ABF has grown within the private credit ecosystem.ABF's market structure and a “trip down main street.”The potential size of the ABF market.Why moving assets off bank balance sheets can help the financial system.Why private credit is a data rich asset.Where ABF fits in a portfolio.Why consumer credit is potentially misunderstood within private credit.Thanks Ivan for coming on the show to share your wisdom and expertise on private credit and ABF. Good luck to anyone keeping up with you on a long run though!You can also see a recent Q&A with Ivan about private credit and ABF on AGM here.Subscribe to Alt Goes Mainstream to receive the weekly newsletter every Sunday and all of AGM's podcasts.A word from AGM podcast sponsor, Ultimus Fund SolutionsThis episode of Alt Goes Mainstream is brought to you by Ultimus Fund Solutions, a leading full-service fund administrator for asset managers in private and public markets. As private markets continue to move into the mainstream, the industry requires infrastructure solutions that help funds and investors keep pace. In an increasingly sophisticated financial marketplace, investment managers must navigate a growing array of challenges: elaborate fund structures, specialized strategies, evolving compliance requirements, a growing need for sophisticated reporting, and intensifying demands for transparency.To assist with these challenging opportunities, more and more fund sponsors and asset managers are turning to Ultimus, a leading service provider that blends high tech and high touch in unique and customized fund administration and middle office solutions for a diverse and growing universe of over 450 clients and 1,800 funds, representing $500 billion assets under administration, all handled by a team of over 1,000 professionals. Ultimus offers a wide range of capabilities across registered funds, private funds and public plans, as well as outsourced middle office services. Delivering operational excellence, Ultimus helps firms manage the ever-changing regulatory environment while meeting the needs of their institutional and retail investors. Ultimus provides comprehensive operational support and fund governance services to help managers successfully launch retail alternative products.Visit www.ultimusfundsolutions.com to learn more about Ultimus' technology enhanced services and solutions or contact Ultimus Executive Vice President of Business Development Gary Harris on email at gharris@ultimusfundsolutions.com.We thank Ultimus for their support of alts going mainstream.Show Notes00:00 Introduction and Message from our Sponsor, Ultimus01:57 Introducing Ivan Zinn03:49 Parallels Between Running and Business05:32 Early Days of Private Credit06:52 Post-GFC Changes in Private Credit07:31 Evolution of Atalaya's Business Model08:21 Growth of Asset-Based Finance09:38 FinTech's Role in Private Credit11:09 Importance of Stable Capital Sources21:09 Concentration Risks in Private Credit22:27 Defining Asset-Based Finance (ABF)22:53 Different Flavors of ABF27:43 Investor Exposure and Risk in Private Credit29:46 Direct Lending vs. Public Credit36:02 Consumer Credit and Perceived Risks37:36 Debunking the Cyclical Perception of Credit Risk38:22 The Utility of Credit Cards During Financial Crises38:44 The Resilience of ABS and Diversified Portfolios39:07 The Role of Data Science in Credit Analysis39:32 Surviving the GFC: A Benchmark for Credit Pools39:53 Diversification in ABF and Private Credit40:48 Selective Approach to Consumer Credit41:36 The Importance of Manager Selection in Credit Investing42:11 Private Market Transactions and Large Announcements42:40 The Journey from Atalaya to Blue Owl43:25 Challenges in Institutional Fundraising and Capital Formation44:20 The Need for Diverse Capital Sources45:43 Integration and Cultural Fit with Blue Owl46:16 The Role of Data Science and Innovation in Credit50:22 The Wealth Channel and Private Credit50:50 Private Credit as a Fixed Income Replacement52:34 Transparency and Market Structure in Private Credit55:55 Educating Investors on Private Credit57:48 The Evolution and Adoption of ABF01:00:15 The Growth of Private Credit Market01:01:28 Challenges and Opportunities in Private Credit01:03:45 The Importance of Scale in Credit Investing01:04:28 Vertical Integration in Financing01:05:26 Relentless Forward Progress in Credit Investing01:06:31 Memorable Investments and Risk-Reward Balance Editing and post-production work for this episode was provided by The Podcast Consultant.

Ever wondered how to stop LLMs from hallucinating or making things up? The answer is RAG (Retrieval-Augmented Generation), and it's a critical technique for building reliable, fact-based AI applications.In this episode, Alan sits down with Abhinav Kimothi, Director of Data Science at Sigmoid and author of the Manning book, "A Simple Guide to RAG". Abhinav demystifies this powerful concept, making it accessible for developers and enthusiasts at any level.This is a must-listen for anyone looking to move beyond basic chatbot functionality and build truly intelligent, trustworthy AI.

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.

If we want AI systems that actually work, we need to get much better at evaluating them, not just building more pipelines, agents, and frameworks. In this episode, Hugo talks with Hamel Hussain (ex-Airbnb, GitHub, DataRobot) about how teams can improve AI products by focusing on error analysis, data inspection, and systematic iteration. The conversation is based on Hamel's blog post A Field Guide to Rapidly Improving AI Products, which he joined Hugo's class to discuss. They cover:

In Episode 29, of Season 5 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Sam Hancock, Global Head of Data Science at Octopus Energy. Sam shares his unconventional career journey from Capital One to Google and Waymo and ultimately to Octopus Energy, where he focuses on leveraging data to drive the company's mission of accelerating the energy transition. The conversation explores the balance between data-driven decision-making and instinct, the unique culture at Octopus Energy, and the company's innovative approach to sustainability and customer service. Sam and Kyle explore the critical role of data and AI in driving sustainability within the energy sector and they discuss the challenges of integrating data into business practices, the importance of fostering relationships across teams, and the innovations in energy management that can help achieve sustainability targets. The conversation also delves into the complexities of carbon emissions, the electrification of industries, and the potential of smart tariffs to optimize energy usage. Overall, the discussion highlights the need for collaboration and innovative solutions to address the pressing challenges of climate change.Takeaways:Sam Hancock's journey includes roles at Capital One, Waymo, and Octopus Energy.He emphasizes the importance of diverse experiences in shaping his current role.Octopus Energy is focused on accelerating the energy transition.The company is customer-obsessed and aims to disrupt the energy sector.Sam's role allows for entrepreneurial freedom in data strategy.Balancing data-driven decisions with instinct is crucial for innovation.Octopus Energy's culture encourages entrepreneurship and high standards.The company has ambitious targets for customer growth and sustainability.Data is a strategic asset at Octopus, with significant data ingestion capabilities.The tech platform developed by Octopus is designed for the energy sector. Understanding the data challenge is crucial for effective pricing.Data integration should start at the beginning of business processes.Building relationships within teams enhances data-driven decision-making.Electrification is key to achieving net-zero emissions.Smart tariffs can optimize energy usage based on grid demands.AI systems are essential for managing the energy transition.Data centres can be a force for good in sustainability efforts.Zonal pricing could incentivize greener energy usage.Companies need to be transparent about their carbon emissions.Nature-based solutions and carbon capture are vital for sustainability.Chapters:00:00 Introduction to Sam Hancock and His Journey01:00 Career Path: From Capital One to Waymo04:30 Transition to Climate Tech and Octopus Energy07:20 Overview of Octopus Energy's Mission and Operations09:48 Sam's Role and Data Strategy at Octopus Energy13:16 Balancing Data and Instinct in Decision Making18:28 The Unique Culture and Vision of Octopus Energy22:12 Leveraging Data as a Strategic Asset25:17 Understanding the Data Challenge28:10 The Role of Data in Sustainability31:31 Innovations in Energy Management37:39 Navigating Carbon Emissions and Sustainability Targets42:15 The Intersection of Data and Energy Solutions...

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

How do you lead at the cutting edge of health, data, and AI—while staying deeply human?Angeli Möller has led global data science teams across pharma giants, co-founded one of Europe's most ambitious AI alliances, and now builds high-performance biotech strategies with precision. But what truly sets her apart isn't just her technical fluency—it's her clarity, courage, and care in how she builds teams, solves problems, and pushes the boundaries of innovation.In this episode, Angeli opens up about the quiet frustrations that fuel her mission, the invisible cost of ignoring innovation, and the principles that guide her client work today. Whether you're an investor, founder, or policymaker, her journey will reshape how you think about leadership, AI, and what truly moves the needle in healthcare.Here's what you'll take away:Why most AI projects fail—and how to spot the ones that won't.How to lead technical teams with vision, warmth, and accountability.Why proprietary data matters more than fancy algorithms.What real innovation feels like—and how to know when you're missing it.At the center of it all: a calm, fiercely smart leader who sees through the noise and builds what matters.As she says: “Start with the real problem. If you don't understand the problem, AI won't help you.”Timestamps & Topics

Her early inspiration while growing up in Goa with limited exposure to career options. Her Father's intellectual influence despite personal hardships and shift in focus to technology.Personal tragedy sparked a resolve to become financially independent and learn deeply.Inspirational quote that shaped her mindset: “Even if your dreams haven't come true, be grateful that so haven't your nightmares.”Her first role at a startup with Hands-on work with networking protocols (LDAP, VPN, DNS). Learning using only RFCs and O'Reilly books—no StackOverflow! Importance of building deep expertise for long-term success.Experiences with Troubleshooting and System Thinking; Transitioned from reactive fixes to logical, structured problem-solving. Her depth of understanding helped in debugging and system optimization.Career move to Yahoo where she led Service Engineering for mobile and ads across global data centers got early exposure to big data and machine learning through ad recommendation systems and built "performance and scale muscle" through working at massive scale.Challenges of Scale and Performance Then vs. Now: Problems remain the same, but data volumes and complexity have exploded. How modern tools (like AI/ML) can help identify relevance and anomalies in large data sets.Design with Scale in Mind - Importance of flipping the design approach: think scale-first, not POC-first. Encourage starting with a big-picture view, even when building a small prototype. Highlights multiple scaling dimensions—data, compute, network, security.Getting Into ML and Data Science with early spark from MOOCs, TensorFlow experiments, and statistics; Transition into data science role at Infoblox, a cybersecurity firm with focus areas on DNS security, anomaly detection, threat intelligence.Building real-world ML model applications like supervised models for threat detection and storage forecasting; developing graph models to analyze DNS traffic patterns for anomalies and key challenges of managing and processing massive volumes of security data.Data stack and what it takes to build data lakes that support ML with emphasis on understanding the end-to-end AI pipelineShifts from “under the hood” ML to front-and-center GenAI & Barriers: Data readiness, ROI, explainability, regulatory compliance.Explainability in AI and importance of interpreting model decisions, especially in regulated industries.How Explainability Works -Trade-offs between interpretable models (e.g., decision trees) and complex ones (e.g., deep learning); Techniques for local and global model understanding.Aruna's Book on Interpretability and Explainability in AI Using Python (by Aruna C).The world of GenAI & Transformers - Explainability in LLMs and GenAI: From attention weights to neuron activation.Challenges of scale: billions of parameters make models harder to interpret. Exciting research areas: Concept tracing, gradient analysis, neuron behavior.GenAI Agents in Action - Transition from task-specific GenAI to multi-step agents. Agents as orchestrators of business workflows using tools + reasoning.Real-world impact of agents and AI for everyday lifeAruna Chakkirala is a seasoned leader with expertise in AI, Data and Cloud. She is an AI Solutions Architect at Microsoft where she was instrumental in the early adoption of Generative AI. In prior roles as a Data Scientist she has built models in cybersecurity and holds a patent in community detection for DNS querying. Through her two-decade career, she has developed expertise in scale, security, and strategy at various organizations such as Infoblox, Yahoo, Nokia, EFI, and Verisign. Aruna has led highly successful teams and thrives on working with cutting-edge technologies. She is a frequent technical and keynote speaker, panelist, author and an active blogger. She contributes to community open groups and serves as a guest faculty member at premier academic institutes. Her book titled "Interpretability and Explainability in AI using Python" covers the taxonomy and techniques for model explanations in AI including the latest research in LLMs. She believes that the success of real-world AI applications increasingly depends on well- defined architectures across all encompassing domains. Her current interests include Generative AI, applications of LLMs and SLMs, Causality, Mechanistic Interpretability, and Explainability tools.Her recently published book linkInterpretability and Explainability in AI Using Python: Decrypt AI Decision-Making Using Interpretability and Explainability with Python to Build Reliable Machine Learning Systems  https://amzn.in/d/00dSOwAOutside of work, she is an avid reader and enjoys creative writing. A passionate advocate for diversity and inclusion, she is actively involved in GHCI, LeanIn communities.

Mark Ericksen, creator of the Elixir LangChain framework, joins the Elixir Wizards to talk about LLM integration in Elixir apps. He explains how LangChain abstracts away the quirks of different AI providers (OpenAI, Anthropic's Claude, Google's Gemini) so you can work with any LLM in one more consistent API. We dig into core features like conversation chaining, tool execution, automatic retries, and production-grade fallback strategies. Mark shares his experiences maintaining LangChain in a fast-moving AI world: how it shields developers from API drift, manages token budgets, and handles rate limits and outages. He also reveals testing tactics for non-deterministic AI outputs, configuration tips for custom authentication, and the highlights of the new v0.4 release, including “content parts” support for thinking-style models. Key topics discussed in this episode: • Abstracting LLM APIs behind a unified Elixir interface • Building and managing conversation chains across multiple models • Exposing application functionality to LLMs through tool integrations • Automatic retries and fallback chains for production resilience • Supporting a variety of LLM providers • Tracking and optimizing token usage for cost control • Configuring API keys, authentication, and provider-specific settings • Handling rate limits and service outages with degradation • Processing multimodal inputs (text, images) in Langchain workflows • Extracting structured data from unstructured LLM responses • Leveraging “content parts” in v0.4 for advanced thinking-model support • Debugging LLM interactions using verbose logging and telemetry • Kickstarting experiments in LiveBook notebooks and demos • Comparing Elixir LangChain to the original Python implementation • Crafting human-in-the-loop workflows for interactive AI features • Integrating Langchain with the Ash framework for chat-driven interfaces • Contributing to open-source LLM adapters and staying ahead of API changes • Building fallback chains (e.g., OpenAI → Azure) for seamless continuity • Embedding business logic decisions directly into AI-powered tools • Summarization techniques for token efficiency in ongoing conversations • Batch processing tactics to leverage lower-cost API rate tiers • Real-world lessons on maintaining uptime amid LLM service disruptions Links mentioned: https://rubyonrails.org/ https://fly.io/ https://zionnationalpark.com/ https://podcast.thinkingelixir.com/ https://github.com/brainlid/langchain https://openai.com/ https://claude.ai/ https://gemini.google.com/ https://www.anthropic.com/ Vertex AI Studio https://cloud.google.com/generative-ai-studio https://www.perplexity.ai/ https://azure.microsoft.com/ https://hexdocs.pm/ecto/Ecto.html https://oban.pro/ Chris McCord's ElixirConf EU 2025 Talk https://www.youtube.com/watch?v=ojL_VHc4gLk Getting started: https://hexdocs.pm/langchain/gettingstarted.html https://ash-hq.org/ https://hex.pm/packages/langchain https://hexdocs.pm/igniter/readme.html https://www.youtube.com/watch?v=WM9iQlQSFg @brainlid on Twitter and BlueSky Special Guest: Mark Ericksen.

In today's episode, I talk to Adam Hendel, the founding engineer of Tembo, about their project, PGMQ, and how it came to be. We discuss the design decisions behind job queues, interfacing from Rust to Postgres, and the engineering decisions that went into building the extension.

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

When deadlines loom, it's easy for data scientists to fall into the trap of cutting corners and bending analyses to deliver what stakeholders want. But what if a simple framework could help you maintain quality under pressure while preserving your professional integrity?In this Value Boost episode, Dr. Brian Godsey joins Dr. Genevieve Hayes to reveal his powerful "Knowledge first, Technology second, Opinions third" hierarchy - a  framework that will transform how you handle stakeholder pressure without compromising your standards.In this episode, you'll discover:Why this critical hierarchy gets dangerously inverted when deadlines loom and how to prevent it from undermining your credibility [01:05]How to resist the career-limiting trap of cherry-picking facts that merely support executive opinions [04:09]A practical note-taking technique that keeps you anchored to reality when stakeholders push for convenient answers [06:04]The one transformative habit that separates truly valuable data scientists from those who merely validate existing assumptions [07:17]Guest BioDr Brian Godsey is a Data Science Lead at AI platform as a service company DataStax. He is also the author of Think Like a Data Scientist and holds a PhD in Mathematical Statistics and Probability.LinksBrian's websiteConnect with Brian on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

This month, we return to a classic Klaviyo Data Science Podcast series: books every data scientist (and software engineer) should read. This episode focuses on the Clean * duology by Robert C. Martin, which teaches the principles of both clean code and clean architecture. We've brought on two senior engineers at Klaviyo who've learned, practiced, and developed their own opinions on the lessons in these books. Listen in to learn:How to use these books to level up your own skills and the skills of your teamWhy the book's spiciest opinions make sense, and where you might disagree with them in practice What our panel's deepest, most intimate thoughts on docstrings areFor more details, including links to these books, check out the ⁠full writeup on Medium⁠!

For the past two years, Jord BioScience has studied five microbial leads in soybean seed treatments to identify biological ingredients that can spur emergence, plant health and yield. Brownfield's Brent Barnett recently sat down with Andrea Arias, Vice President of Data Science & Crop Testing with Jord BioScience to learn more about the trials and the company.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Luis Poggi is the CEO and Co-Founder of HouseWhisper, a startup at the intersection of real estate and generative AI, building tools that transform how homes are marketed and sold. A seasoned tech executive with deep experience in product, marketing, and sales, Luis previously held leadership roles at Zillow and Expedia, where he helped scale industry-defining platforms in PropTech and travel.Now focused on shaping the AI revolution in real estate, Luis blends entrepreneurial vision with hands-on execution. He also shares insights on AI and business strategy through his newsletter at substack.com/luispoggi.(01:50) - Luis' Zillow journey & lessons(02:53) - The Birth of House Whisper(04:30) - The power of zero onboarding & personalization(05:11) - AI Agents & the Future of Real Estate(09:19) - Challenges & opportunities in AI for Real Estate Agents like Serhant(14:09) - Distribution strategy(16:29) - Will AI replace Real Estate agents like travel agents?(17:48) - 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. See if you qualify and apply by emailing tangentcommunity@gmail.com.(19:14) - Avoiding the freemium pricing trap(22:48) - Usage & retention: 8K+ paying agents(29:22) - Collaboration Superpower: Andrej Karpathy (OpenAI Co-founder, Wiki)

Topics covered in this episode: platformdirs poethepoet - “Poe the Poet is a batteries included task runner that works well with poetry or with uv.” Python Pandas Ditches NumPy for Speedier PyArrow pointblank: Data validation made beautiful and powerful 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: platformdirs A small Python module for determining appropriate platform-specific dirs, e.g. a "user data dir". Why the community moved on from appdirs to platformdirs At AppDirs: Note: This project has been officially deprecated. You may want to check out pypi.org/project/platformdirs/ which is a more active fork of appdirs. Thanks to everyone who has used appdirs. Shout out to ActiveState for the time they gave their employees to work on this over the years. Better than AppDirs: Works today, works tomorrow – new Python releases sometimes change low-level APIs (win32com, pathlib, Apple sandbox rules). platformdirs tracks those changes so your code keeps running. First-class typing – no more types-appdirs stubs; editors autocomplete paths as Path objects. Richer directory set – if you need a user's Downloads folder or a per-session runtime dir, there's a helper for it. Cleaner internals – rewritten to use pathlib, caching, and extensive test coverage; all platforms are exercised in CI. Community stewardship – the project lives in the PyPA orbit and gets security/compatibility patches quickly. Brian #2: poethepoet - “Poe the Poet is a batteries included task runner that works well with poetry or with uv.” from Bob Belderbos Tasks are easy to define and are defined in pyproject.toml Michael #3: Python Pandas Ditches NumPy for Speedier PyArrow Pandas 3.0 will significantly boost performance by replacing NumPy with PyArrow as its default engine, enabling faster loading and reading of columnar data. Recently talked with Reuven Lerner about this on Talk Python too. In the next version, v3.0, PyArrow will be a required dependency, with pyarrow.string being the default type inferred for string data. PyArrow is 10 times faster. PyArrow offers columnar storage, which eliminates all that computational back and forth that comes with NumPy. PyArrow paves the way for running Pandas, by default, on Copy on Write mode, which improves memory and performance usage. Brian #4: pointblank: Data validation made beautiful and powerful “With its … chainable API, you can … validate your data against comprehensive quality checks …” Extras Brian: Ruff rules Ruff users, what rules are using and what are you ignoring? Python 3.14.0b2 - did we already cover this? Transferring your Mastodon account to another server, in case anyone was thinking about doing that I'm trying out Fathom Analytics for privacy friendly analytics Michael: Polars for Power Users: Transform Your Data Analysis Game Course Joke: Does your dog bite?

As Infosecurity Europe prepares to mark its 30th anniversary, Portfolio Director Saima Poorghobad shares how the event continues to evolve to meet the needs of cybersecurity professionals across industries, sectors, and career stages. What began in 1996 as a niche IT gathering has grown into a strategic hub for over 14,000 visitors, offering much more than just vendor booths and keynotes. Saima outlines how the event has become a dynamic space for learning, collaboration, and strategic alignment—balancing deep technical insight with the broader social, political, and technological shifts impacting the cybersecurity community.The Power of the Crowd: Community, Policy, and Lifelong LearningThis year's programming reflects the diverse needs of the cybersecurity community. Attendees range from early-career practitioners to seasoned decision-makers, with representation growing from academia and public policy. The UK government will participate in sessions designed to engage with the community and gather feedback to inform future regulation—a sign of how the show has expanded beyond its commercial roots. Universities are also getting special attention, with new student guides and tailored experiences to help emerging professionals find their place in the ecosystem.Tackling Today's and Tomorrow's Threats—From Quantum to GeopoliticsInfosecurity Europe 2024 is not shying away from bold topics. Professor Brian Cox will open the event by exploring the intersection of quantum science and cybersecurity, setting the tone for a future-facing agenda. Immediately following, BBC's Joe Tidy will moderate a session on how organizations can prepare for the cryptographic disruption quantum computing could bring. Rory Stewart will bring a geopolitical lens to the conversation, examining how shifting alliances, global trade tensions, and international conflicts are reshaping the threat landscape and influencing cybersecurity priorities across regions.Maximizing the Experience: Prep, Participate, and PartyFrom hands-on tech demos to peer-led table talks and new formats like the AI and Cloud Security Theater, the show is designed to be navigable—even for first-time attendees. Saima emphasizes preparation, networking, and follow-up as keys to success, with a new content download feature helping attendees retain insights post-event. The celebration culminates with a 90s-themed 30th anniversary party and a strong sense of pride in what this event has helped the community build—and protect—over three decades.The message is clear: cybersecurity is no longer just a technical field—it's a societal one.___________Guest: Saima Poorghobad, Portfolio Director at Reed Exhibitions | https://www.linkedin.com/in/saima-poorghobad-6a37791b/ Hosts:Sean Martin, Co-Founder at ITSPmagazine | Website: https://www.seanmartin.comMarco Ciappelli, Co-Founder at ITSPmagazine | Website: https://www.marcociappelli.com___________Episode SponsorsThreatLocker: https://itspm.ag/threatlocker-r974___________ResourcesLearn more and catch more stories from Infosecurity Europe 2025 London coverage: https://www.itspmagazine.com/infosec25Catch all of our event coverage: https://www.itspmagazine.com/technology-and-cybersecurity-conference-coverageWant to tell your Brand Story Briefing as part of our event coverage? Learn More

Dr Liberty Vittert Capito is a Professor of the Practice of Data Science at the Olin Business School at the Washington University in St. Louis, and is a senior fellow at Harvard and MIT. She's probably one of the world's only great statisticians and data scientists who has a degree from one of the best cooking schools in the world. We'll discuss a piece she wrote for The Free Press entitled "Why I Left the UN Fundraising Group My Father Helped Found"From a news story about the anti-Semitic troll known as Greta Thunberg, "Francesca Albanese, United Nations' special rapporteur on human rights in the Palestinian territories, also urged other boats to challenge the Gaza blockade. She said on social media: 'Madleen's journey may have ended, but the mission isn't over. Every Mediterranean port must send boats with aid & solidarity to Gaza.'"Olin Business School | Liberty VittertLiberty has gotten married since she was last on the show and her little brother, Leland, got married this past weekend!

If you've heard the phrase "Automate the boring things" for Python, this episode starts with that idea and takes it to another level. We have Glyph back on the podcast to talk about "Programming YOUR computer with Python." We dive into a bunch of tools and frameworks and especially spend some time on integrating with existing platform APIs (e.g. macOS's BrowserKit and Window's COM APIs) to build desktop apps in Python that make you happier and more productive. Let's dive in! Episode sponsors Posit Agntcy Talk Python Courses Links from the show Glyph on Mastodon: @glyph@mastodon.social Glyph on GitHub: github.com/glyph Glyph's Conference Talk: LceLUPdIzRs: youtube.com Notify Py: ms7m.github.io Rumps: github.com QuickMacHotkey: pypi.org QuickMacApp: pypi.org LM Studio: lmstudio.ai Coolify: coolify.io PyWin32: pypi.org WinRT: pypi.org PyObjC: pypi.org PyObjC Documentation: pyobjc.readthedocs.io Watch this episode on YouTube: youtube.com 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

NBA Game One, 15 NHL Stanley Cup Final, 25 Ben Brown, 36 More NHL talk

Today's guest is Damion Nero, Head of Data Science at Takeda Pharmaceuticals. With over 15 years of experience applying AI, machine learning, and real-world data to drug development and precision medicine, Damion joins Emerj Managing Editor Matthew DeMello to explore the evolving role of AI in drug development and supply chain management. He breaks down how AI is currently streamlining administrative and regulatory tasks, improving efficiency across clinical trials, and saving valuable time for healthcare professionals. Damion also discusses why broader, transformative supply chain efficiencies are still on the horizon, as AI continues to evolve and scale in the pharmaceutical industry. This episode is sponsored by Arkestro. Learn more about Arkestro's upcoming Advisory Council event here. Find out more about sponsored content and how to engage with the Emerj audience at emerj.com/ad1.

Join us for an exciting episode of The Edge of Show, live from Token 2049 in Dubai! In this episode, we have insightful conversations with industry leaders, including Alex Svanevik , CEO and co-founder of Nansen, and Kamal Youssefi, Executive Chairman of Hedera, as well as David Chen, co-founder of Geodnet.Key takeaways:The evolving landscape of on-chain analytics, the competition among blockchain networks, and the significance of on-chain activity in relation to token value. The growth of the Hedera ecosystem, the importance of digital identity in Web3, and the collaborative efforts of the Hedera Governing Council. He shares exciting use cases and the vision for tokenized equity.GeoNet, a groundbreaking project that enhances GPS accuracy using RTK technology and how is paving the way for Web3 innovations.Tune in to gain valuable insights into the future of blockchain technology, the importance of building great products, and the potential of decentralized solutions in various industries. Don't forget to like, subscribe, and hit the notification bell for more episodes! Support us through our Sponsors! ☕

Today's guest is Xiong Liu, Director of Data Science and AI at Novartis. Xiong returns to the platform in a special episode brought to you by Medable to explore the evolving role of AI in modernizing clinical trials. Their conversation covers how life sciences teams are leveraging AI to streamline data workflows, accelerate study readiness, and maintain regulatory compliance in both decentralized and traditional models. Throughout the episode, Xiong shares insights into the growing importance of integrating structured and unstructured data across trial systems — from clinical notes to imaging and lab results. This episode is sponsored by Medable. Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1. 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!

The data science world has always been obsessed with tools and techniques - a fixation that's only intensified in the era of generative AI. Yet even as ChatGPT and similar technologies transform the landscape, the fundamental challenge remains the same - turning technical capabilities into business results requires a process most data scientists never learned.In this episode, Dr. Brian Godsey joins Dr. Genevieve Hayes to discuss why the scientific process behind data science remains more critical than ever, sharing how his original "Think Like a Data Scientist" framework has evolved to harness today's powerful AI capabilities while maintaining the principles that drive real business values.This conversation reveals:Why the seemingly basic question "Where do I start?" continues to derail data scientists' effectiveness and how mastering the right process can transform your impact [01:15]The three stages of the data science process that remain essential for career success even as AI dramatically changes how quickly you can execute them [11:07]How the accessibility revolution of generative AI creates new career opportunities for data scientists in organizations that previously couldn't leverage advanced analytics [18:34]The underrated troubleshooting skill that will make you invaluable as organizations increasingly rely on "black box" AI models for business-critical decisions [20:21]Guest BioDr Brian Godsey is a Data Science Lead at AI platform as a service company DataStax. He is also the author of Think Like a Data Scientist and holds a PhD in Mathematical Statistics and Probability.LinksBrian's websiteConnect with Brian on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Send us a textOn today's episode of Serious Angler's Reel Biology we are joined by Ray Valley to talk about BioBase and the new advancements in fish data and fish mapping layers and how this will advance what we know about fish species.

If you want to leverage the power of LLMs in your Python apps, you would be wise to consider an agentic framework. Agentic empowers the LLMs to use tools and take further action based on what it has learned at that point. And frameworks provide all the necessary building blocks to weave these into your apps with features like long-term memory and durable resumability. I'm excited to have Sydney Runkle back on the podcast to dive into building Python apps with LangChain and LangGraph. Episode sponsors Posit Auth0 Talk Python Courses Links from the show Sydney Runkle: linkedin.com LangGraph: github.com LangChain: langchain.com LangGraph Studio: github.com LangGraph (Web): langchain.com LangGraph Tutorials Introduction: langchain-ai.github.io How to Think About Agent Frameworks: blog.langchain.dev Human in the Loop Concept: langchain-ai.github.io GPT-4 Prompting Guide: cookbook.openai.com Watch this episode on YouTube: youtube.com 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

Topics covered in this episode: Making PyPI's test suite 81% faster People aren't talking enough about how most of OpenAI's tech stack runs on Python PyCon Talks on YouTube Optimizing Python Import Performance Extras Joke Watch on YouTube About the show Sponsored by Digital Ocean: 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: Making PyPI's test suite 81% faster Alexis Challande The PyPI backend is a project called Warehouse It's tested with pytest, and it's a large project, thousands of tests. Steps for speedup Parallelizing test execution with pytest-xdist 67% time reduction --numprocesses=auto allows for using all cores DB isolation - cool example of how to config postgress to give each test worker it's on db They used pytest-sugar to help with visualization, as xdist defaults to quite terse output Use Python 3.12's sys.monitoring to speed up coverage instrumentation 53% time reduction Nice example of using COVERAGE_CORE=sysmon Optimize test discovery Always use testpaths Sped up collection time. 66% reduction (collection was 10% of time) Not a huge savings, but it's 1 line of config Eliminate unnecessary imports Use python -X importtime Examine dependencies not used in testing. Their example: ddtrace A tool they use in production, but it also has a couple pytest plugins included Those plugins caused ddtrace to get imported Using -p:no ddtrace turns off the plugin bits Notes from Brian: I often get questions about if pytest is useful for large projects. Short answer: Yes! Longer answer: But you'll probably want to speed it up I need to extend this article with a general purpose “speeding up pytest” post or series. -p:no can also be used to turn off any plugin, even builtin ones. Examples include nice to have developer focused pytest plugins that may not be necessary in CI CI reporting plugins that aren't needed by devs running tests locally Michael #2: People aren't talking enough about how most of OpenAI's tech stack runs on Python Original article: Building, launching, and scaling ChatGPT Images Tech stack: The technology choices behind the product are surprisingly simple; dare I say, pragmatic! Python: most of the product's code is written in this language. FastAPI: the Python framework used for building APIs quickly, using standard Python type hints. As the name suggests, FastAPI's strength is that it takes less effort to create functional, production-ready APIs to be consumed by other services. C: for parts of the code that need to be highly optimized, the team uses the lower-level C programming language Temporal: used for asynchronous workflows and operations inside OpenAI. Temporal is a neat workflow solution that makes multi-step workflows reliable even when individual steps crash, without much effort by developers. It's particularly useful for longer-running workflows like image generation at scale Michael #3: PyCon Talks on YouTube Some talks that jumped out to me: Keynote by Cory Doctorow 503 days working full-time on FOSS: lessons learned Going From Notebooks to Scalable Systems And my Talk Python conversation around it. (edited episode pending) Unlearning SQL The Most Bizarre Software Bugs in History The PyArrow revolution in Pandas And my Talk Python episode about it. What they don't tell you about building a JIT compiler for CPython And my Talk Python conversation around it (edited episode pending) Design Pressure: The Invisible Hand That Shapes Your Code Marimo: A Notebook that "Compiles" Python for Reproducibility and Reusability And my Talk Python episode about it. GPU Programming in Pure Python And my Talk Python conversation around it (edited episode pending) Scaling the Mountain: A Framework for Tackling Large-Scale Tech Debt Brian #4: Optimizing Python Import Performance Mostly pay attention to #'s 1-3 This is related to speeding up a test suite, speeding up necessary imports. Finding what's slow Use python -X importtime

Dr. Martin Goodson is the founder and CEO of Evolution AI, a company he launched in 2012 to apply deep learning to optical character recognition (OCR). The company has received one of the largest AI R&D grants ever awarded by the UK government, along with investment from First Minute Capital. A former scientific researcher at Oxford University, Martin has led AI research across several organizations and was elected Chair of the Data Science and AI Section of the Royal Statistical Society in 2019.In this conversation, we discuss:Martin Goodson's journey from researching biological data to founding Evolution AI and pioneering deep learning for document understanding.Why traditional OCR missed the mark, and how combining visual and linguistic context unlocked a new frontier in document intelligence.The evolution from data extraction to true financial analysis, and why domain knowledge is essential for reading statements like income reports.The risks of LLM hallucinations, especially with numerical data, and why accuracy still requires combining techniques across model types.What Martin believes intelligence really is, and why language alone may be the wrong benchmark for AGI.Why recreating human intelligence shouldn't be the goal of AI research, and how we can build systems that support, not mimic, human thinking.Resources:Subscribe to the AI & The Future of Work NewsletterConnect with Martin on LinkedInCheck out the YouTube channel of the London Machine Learning MeetupAI fun fact articleOn How to Ovecome Imposter SyndromePast episodes mentioned:On Why doing Taxes is like finding the Best Route on a Map with Daniel MarcousOn Making AI Smarter Without Harming Humans with Peter Voss

In this podcast, we've partnered with a data science pharmacist to explore challenges that can arise when implementing artificial intelligence (AI) in pharmacy. We'll focus on his experience with AI governance, ethical challenges, and key considerations for the everyday pharmacist.   The information presented during the podcast reflects solely the opinions of the presenter. The information and materials are not, and are not intended as, a comprehensive source of drug information on this topic. The contents of the podcast have not been reviewed by ASHP, and should neither be interpreted as the official policies of ASHP, nor an endorsement of any product(s), nor should they be considered as a substitute for the professional judgment of the pharmacist or physician.

Dr. Dan interviews Dr. Avriel Epps, a dynamic scholar, author, and strategist whose work sits at the crossroads of transformative justice and artificial intelligence. With a PhD in Human Development and a masters in Data Science from Harvard University, Dr. Epps brings a fresh and critical perspective to conversations about technology, equity, and social justice. On today's episode, Dr. Dan and Dr. Epps explore her work around how bias in predictive technologies affects racial, gender, and sociopolitical identity development. She aims to understand the complex ways that algorithm design and computer-mediated social expectations—often communicated through artificial intelligence systems—impact the beliefs, behaviors, and health of developing humans. On today's episode, listeners will hear explanations and examples about how AI can sometimes reinforce unfairness. Dr. Dan and Dr. Epps urge us to be part of the solution by demanding technology that works for everyone, not just a few. Dr. Avriel Epps is a former child actor and an R&B artist turned algorithmic justice expert. Her work shows us that AI is not neutral, reminds us algorithmic bias impacts are real, and urges us to question technology.  In the Fall of 2025, she will begin her tenure as Assistant Professor of Fair and Responsible Data Science at Rutgers University. For more information www.avrielepps.com and follow @kingavriel on Instagram. Please listen, follow, rate, and review Make It a Great One on Apple Podcasts, Spotify, or wherever you listen to podcasts. Follow @drdanpeters on social media. Visit www.drdanpeters.com and send your questions or guest pitches to podcast@drdanpeters.com. We have this moment, this day, and this life—let's make it a great one. – Dr. Dan Learn more about your ad choices. Visit podcastchoices.com/adchoices

Andrew Heiss is an assistant professor in the Department of Public Management and Policy at the Andrew Young School of Policy Studies at Georgia State University. Vincent's “What is your estimand” section in his {marginaleffects} book: https://marginaleffects.com/chapters/challenge.html#sec-goals_estimand Article on defining estimands: https://doi.org/10.1177/00031224211004187 Andrew's marginal effects post: https://www.andrewheiss.com/blog/2022/05/20/marginalia/ Andrew's post on “fixed effects” and mariginal effects across different disciplines: https://www.andrewheiss.com/blog/2022/11/29/conditional-marginal-marginaleffects/ Follow along on Bluesky: Andrew: @andrew.heiss.phd Ellie: @epiellie.bsky.social Lucy: @lucystats.bsky.social  

Topics covered in this episode: git-flight-rules Uravelling t-strings neohtop Introducing Pyrefly: A new type checker and IDE experience for Python 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: git-flight-rules What are "flight rules"? A guide for astronauts (now, programmers using Git) about what to do when things go wrong. Flight Rules are the hard-earned body of knowledge recorded in manuals that list, step-by-step, what to do if X occurs, and why. Essentially, they are extremely detailed, scenario-specific standard operating procedures. [...] NASA has been capturing our missteps, disasters and solutions since the early 1960s, when Mercury-era ground teams first started gathering "lessons learned" into a compendium that now lists thousands of problematic situations, from engine failure to busted hatch handles to computer glitches, and their solutions. Steps for common operations and actions I want to start a local repository What did I just commit? I want to discard specific unstaged changes Restore a deleted file Brian #2: Uravelling t-strings Brett Cannon Article walks through Evaluating the Python expression Applying specified conversions Applying format specs Using an Interpolation class to hold details of replacement fields Using Template class to hold parsed data Plus, you don't have to have Python 3.14.0b1 to try this out. The end result is very close to an example used in PEP 750, which you do need 3.14.0b1 to try out. See also: I've written a pytest version, Unravelling t-strings with pytest, if you want to run all the examples with one file. Michael #3: neohtop Blazing-fast system monitoring for your desktop Features Real-time process monitoring CPU and Memory usage tracking Beautiful, modern UI with dark/light themes Advanced process search and filtering Pin important processes Process management (kill processes) Sort by any column Auto-refresh system stats Brian #4: Introducing Pyrefly: A new type checker and IDE experience for Python From Facebook / Meta Another Python type checker written in Rust Built with IDE integration in mind from the beginning Principles Performance IDE first Inference (inferring types in untyped code) Open source I mistakenly tried this on the project I support with the most horrible abuses of the dynamic nature of Python, pytest-check. It didn't go well. But perhaps the project is ready for some refactoring. I'd like to try it soon on a more well behaved project. Extras Brian: Python: The Documentary Official Trailer Tim Hopper added Setting up testing with ptyest and uv to his “Python Developer Tooling Handbook” For a more thorough intro on pytest, check out courses.pythontest.com pocket is closing, I'm switching to Raindrop I got one question about code formatting. It's not highlighted, but otherwise not bad. Michael: New course! Polars for Power Users: Transform Your Data Analysis Game Apache Airflow 3.0 Released Paste 5 Joke: Theodore Roosevelt's Man in the Arena, but for programming