Episode 39: From Models To Products: Bridging Research And Practice In Generative AI At Google Labs Vanishing Gradients podcast

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What if the cost of writing code dropped to zero — but the cost of understanding it skyrocketed? In this episode, Hugo sits down with Joe Reis to unpack how AI tooling is reshaping the software development lifecycle — from experimentation and prototyping to deployment, maintainability, and everything in between. Joe is the co-author of Fundamentals of Data Engineering and a longtime voice on the systems side of modern software. He’s also one of the sharpest critics of “vibe coding” — the emerging pattern of writing software by feel, with heavy reliance on LLMs and little regard for structure or quality. We dive into: • Why “vibe coding” is more than a meme — and what it says about how we build today • How AI tools expand the surface area of software creation — for better and worse • What happens to technical debt, testing, and security when generation outpaces understanding • The changing definition of “production” in a world of ephemeral, internal, or just-good-enough tools • How AI is flattening the learning curve — and threatening the talent pipeline • Joe’s view on what real craftsmanship means in an age of disposable code This conversation isn’t about doom, and it’s not about hype. It’s about mapping the real, messy terrain of what it means to build software today — and how to do it with care. LINKS Joe's Practical Data Modeling Newsletter on Substack Joe's Practical Data Modeling Server on Discord Vanishing Gradients YouTube Channel Upcoming Events on Luma 🎓 Want to go deeper? Check out my course: Building LLM Applications for Data Scientists and Software Engineers. Learn how to design, test, and deploy production-grade LLM systems — with observability, feedback loops, and structure built in. This isn’t about vibes or fragile agents. It’s about making LLMs reliable, testable, and actually useful. Includes over $2,500 in compute credits and guest lectures from experts at DeepMind, Moderna, and more. Cohort starts April 7 — Use this link for a 10% discount…

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Episode 46: Software Composition Is the New Vibe Coding 1:08:57

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What if building software felt more like composing than coding? In this episode, Hugo and Greg explore how LLMs are reshaping the way we think about software development—from deterministic programming to a more flexible, prompt-driven, and collaborative style of building. It’s not just hype or grift—it’s a real shift in how we express intent, reason about systems, and collaborate across roles. Hugo speaks with Greg Ceccarelli—co-founder of SpecStory, former CPO at Pluralsight, and Director of Data Science at GitHub—about the rise of software composition and how it changes the way individuals and teams create with LLMs. We dive into: Why software composition is emerging as a serious alternative to traditional coding The real difference between vibe coding and production-minded prototyping How LLMs are expanding who gets to build software—and how What changes when you focus on intent, not just code What Greg is building with SpecStory to support collaborative, traceable AI-native workflows The challenges (and joys) of debugging and exploring with agentic tools like Cursor and Claude We’ve removed the visual demos from the audio—but you can catch our live-coded Chrome extension and JFK document explorer on YouTube. Links below. JFK Docs Vibe Coding Demo (YouTube) Chrome Extension Vibe Coding Demo (YouTube) Meditations on Tech (Greg’s Substack) Simon Willison on Vibe Coding Johnno Whitaker: On Vibe Coding Tim O’Reilly – The End of Programming Vanishing Gradients YouTube Channel Upcoming Events on Luma Greg Ceccarelli on LinkedIn Greg’s Hacker News Post on GOOD SpecStory: GOOD – Git Companion for AI Workflows 🎓 Want to go deeper? Check out my course: Building LLM Applications for Data Scientists and Software Engineers. Learn how to design, test, and deploy production-grade LLM systems — with observability, feedback loops, and structure built in. This isn’t about vibes or fragile agents. It’s about making LLMs reliable, testable, and actually useful. Includes over $2,500 in compute credits and guest lectures from experts at DeepMind, Moderna, and more. Cohort starts April 7 — Use this link for a 10% discount 🔍 Want to help shape the future of SpecStory? Greg and the team are looking for design partners for their new SpecStory Teams product—built for collaborative, AI-native software development. If you're working with LLMs in a team setting and want to influence the next wave of developer tools, you can apply here: 👉 specstory.com/teams…

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Episode 45: Your AI application is broken. Here’s what to do about it. 1:17:30

for 9 weeks siden1:17:30

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Too many teams are building AI applications without truly understanding why their models fail. Instead of jumping straight to LLM evaluations, dashboards, or vibe checks, how do you actually fix a broken AI app? In this episode, Hugo speaks with Hamel Husain , longtime ML engineer, open-source contributor, and consultant, about why debugging generative AI systems starts with looking at your data . In this episode, we dive into: Why “look at your data” is the best debugging advice no one follows. How spreadsheet-based error analysis can uncover failure modes faster than complex dashboards. The role of synthetic data in bootstrapping evaluation. When to trust LLM judges —and when they’re misleading. Why most AI dashboards measuring truthfulness, helpfulness, and conciseness are often a waste of time. If you're building AI-powered applications, this episode will change how you approach debugging, iteration, and improving model performance in production. LINKS The podcast livestream on YouTube Hamel's blog Hamel on twitter Hugo on twitter Vanishing Gradients on twitter Vanishing Gradients on YouTube Vanishing Gradients on Twitter Vanishing Gradients on Lu.ma Building LLM Application for Data Scientists and SWEs, Hugo course on Maven (use VG25 code for 25% off) Hugo is also running a free lightning lesson next week on LLM Agents: When to Use Them (and When Not To )…

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Episode 44: The Future of AI Coding Assistants: Who’s Really in Control? 1:34:11

for 12 weeks siden1:34:11

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AI coding assistants are reshaping how developers write, debug, and maintain code—but who’s really in control? In this episode, Hugo speaks with Tyler Dunn , CEO and co-founder of Continue , an open-source AI-powered code assistant that gives developers more customization and flexibility in their workflows. In this episode, we dive into: The trade-offs between proprietary vs. open-source AI coding assistants —why open-source might be the future. How structured workflows, modular AI, and customization help developers maintain control over their tools . The evolution of AI-powered coding, from autocomplete to intelligent code suggestions and beyond. Why the best developer experiences come from sensible defaults with room for deeper configuration. The future of LLM-based software engineering , where fine-tuning models on personal and team-level data could make AI coding assistants even more effective. With companies increasingly integrating AI into development workflows, this conversation explores the real impact of these tools—and the importance of keeping developers in the driver's seat. LINKS The podcast livestream on YouTube Continue's website Continue is hiring! amplified.dev: We believe in a future where developers are amplified, not automated Beyond Prompt and Pray, Building Reliable LLM-Powered Software in an Agentic World LLMOps Lessons Learned: Navigating the Wild West of Production LLMs 🚀 Building effective agents by Erik Schluntz and Barry Zhang, Anthropic Ty on LinkedIn Hugo on twitter Vanishing Gradients on twitter Vanishing Gradients on YouTube Vanishing Gradients on Twitter Vanishing Gradients on Lu.ma…

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Episode 43: Tales from 400+ LLM Deployments: Building Reliable AI Agents in Production 1:01:03

for 14 weeks siden1:01:03

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Hugo speaks with Alex Strick van Linschoten, Machine Learning Engineer at ZenML and creator of a comprehensive LLMOps database documenting over 400 deployments. Alex's extensive research into real-world LLM implementations gives him unique insight into what actually works—and what doesn't—when deploying AI agents in production. In this episode, we dive into: The current state of AI agents in production, from successes to common failure modes Practical lessons learned from analyzing hundreds of real-world LLM deployments How companies like Anthropic, Klarna, and Dropbox are using patterns like ReAct, RAG, and microservices to build reliable systems The evolution of LLM capabilities, from expanding context windows to multimodal applications Why most companies still prefer structured workflows over fully autonomous agents We also explore real-world case studies of production hurdles, including cascading failures, API misfires, and hallucination challenges. Alex shares concrete strategies for integrating LLMs into your pipelines while maintaining reliability and control. Whether you're scaling agents or building LLM-powered systems, this episode offers practical insights for navigating the complex landscape of LLMOps in 2025. LINKS The podcast livestream on YouTube The LLMOps database All blog posts about the database Anthropic's Building effective agents essay Alex on LinkedIn Hugo on twitter Vanishing Gradients on twitter Vanishing Gradients on YouTube Vanishing Gradients on Twitter Vanishing Gradients on Lu.ma…

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Episode 42: Learning, Teaching, and Building in the Age of AI 1:20:03

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In this episode of Vanishing Gradients, the tables turn as Hugo sits down with Alex Andorra, host of Learning Bayesian Statistics. Hugo shares his journey from mathematics to AI, reflecting on how Bayesian inference shapes his approach to data science, teaching, and building AI-powered applications. They dive into the realities of deploying LLM applications, overcoming “proof-of-concept purgatory,” and why first principles and iteration are critical for success in AI. Whether you’re an educator, software engineer, or data scientist, this episode offers valuable insights into the intersection of AI, product development, and real-world deployment. LINKS The podcast on YouTube The original podcast episode Alex Andorra on LinkedIn Hugo on LinkedIn Hugo on twitter Vanishing Gradients on twitter Hugo's "Building LLM Applications for Data Scientists and Software Engineers" course…

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Episode 41: Beyond Prompt Engineering: Can AI Learn to Set Its Own Goals? 43:51

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Hugo Bowne-Anderson hosts a panel discussion from the MLOps World and Generative AI Summit in Austin, exploring the long-term growth of AI by distinguishing real problem-solving from trend-based solutions. If you're navigating the evolving landscape of generative AI, productionizing models, or questioning the hype, this episode dives into the tough questions shaping the field. The panel features: Ben Taylor (Jepson) – CEO and Founder at VEOX Inc., with experience in AI exploration, genetic programming, and deep learning. Joe Reis – Co-founder of Ternary Data and author of Fundamentals of Data Engineering . Juan Sequeda – Principal Scientist and Head of AI Lab at Data.World, known for his expertise in knowledge graphs and the semantic web. The discussion unpacks essential topics such as: The shift from prompt engineering to goal engineering —letting AI iterate toward well-defined objectives. Whether generative AI is having an electricity moment or more of a blockchain trajectory . The combinatorial power of AI to explore new solutions, drawing parallels to AlphaZero redefining strategy games. The POC-to-production gap and why AI projects stall. Failure modes, hallucinations, and governance risks —and how to mitigate them. The disconnect between executive optimism and employee workload. Hugo also mentions his upcoming workshop on escaping Proof-of-Concept Purgatory , which has evolved into a Maven course "Building LLM Applications for Data Scientists and Software Engineers" launching in January . Vanishing Gradient listeners can get 25% off the course (use the code VG25), with $1,000 in Modal compute credits included. A huge thanks to Dave Scharbach and the Toronto Machine Learning Society for organizing the conference and to the audience for their thoughtful questions. As we head into the new year, this conversation offers a reality check amidst the growing AI agent hype. LINKS Hugo on twitter Hugo on LinkedIn Vanishing Gradients on twitter "Building LLM Applications for Data Scientists and Software Engineers" course .…

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Episode 40: What Every LLM Developer Needs to Know About GPUs 1:43:34

for 18 weeks siden1:43:34

1:43:34

Hugo speaks with Charles Frye , Developer Advocate at Modal and someone who really knows GPUs inside and out. If you’re a data scientist, machine learning engineer, AI researcher, or just someone trying to make sense of hardware for LLMs and AI workflows , this episode is for you. Charles and Hugo dive into the practical side of GPUs —from running inference on large models, to fine-tuning and even training from scratch. They unpack the real pain points developers face, like figuring out: How much VRAM you actually need. Why memory—not compute—ends up being the bottleneck. How to make quick, back-of-the-envelope calculations to size up hardware for your tasks. And where things like fine-tuning, quantization, and retrieval-augmented generation (RAG) fit into the mix. One thing Hugo really appreciate is that Charles and the Modal team recently put together the GPU Glossary —a resource that breaks down GPU internals in a way that’s actually useful for developers. We reference it a few times throughout the episode, so check it out in the show notes below. 🔧 Charles also does a demo during the episode —some of it is visual, but we talk through the key points so you’ll still get value from the audio. If you’d like to see the demo in action, check out the livestream linked below. This is the "Building LLM Applications for Data Scientists and Software Engineers" course that Hugo is teaching with Stefan Krawczyk (ex-StitchFix) in January . Charles is giving a guest lecture at on hardware for LLMs, and Modal is giving all students $1K worth of compute credits (use the code VG25 for $200 off). LINKS The livestream on YouTube The GPU Glossary by the Modal team What We’ve Learned From A Year of Building with LLMs by Charles and friends Charles on twitter Hugo on twitter Vanishing Gradients on twitter…

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Episode 39: From Models to Products: Bridging Research and Practice in Generative AI at Google Labs 1:43:28

for 22 weeks siden1:43:28

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Hugo speaks with Ravin Kumar,*Senior Research Data Scientist at Google Labs. Ravin’s career has taken him from building rockets at SpaceX to driving data science and technology at Sweetgreen, and now to advancing generative AI research and applications at Google Labs and DeepMind. His multidisciplinary experience gives him a rare perspective on building AI systems that combine technical rigor with practical utility. In this episode, we dive into: • Ravin’s fascinating career path, including the skills and mindsets needed to work effectively with AI and machine learning models at different stages of the pipeline. • How to build generative AI systems that are scalable, reliable, and aligned with user needs. • Real-world applications of generative AI, such as using open weight models such as Gemma to help a bakery streamline operations—an example of delivering tangible business value through AI. • The critical role of UX in AI adoption, and how Ravin approaches designing tools like Notebook LM with the user journey in mind. We also include a live demo where Ravin uses Notebook LM to analyze my website, extract insights, and even generate a podcast-style conversation about me. While some of the demo is visual, much can be appreciated through audio, and we’ve added a link to the video in the show notes for those who want to see it in action. We’ve also included the generated segment at the end of the episode for you to enjoy. LINKS The livestream on YouTube Google Labs Ravin's GenAI Handbook Breadboard: A library for prototyping generative AI applications As mentioned in the episode, Hugo is teaching a four-week course, Building LLM Applications for Data Scientists and SWEs , co-led with Stefan Krawczyk (Dagworks, ex-StitchFix). The course focuses on building scalable, production-grade generative AI systems, with hands-on sessions, $1,000+ in cloud credits, live Q&As, and guest lectures from industry experts. Listeners of Vanishing Gradients can get 25% off the course using this special link or by applying the code VG25 at checkout.…

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Episode 38: The Art of Freelance AI Consulting and Products: Data, Dollars, and Deliverables 1:23:47

for 25 weeks siden1:23:47

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Hugo speaks with Jason Liu, an independent AI consultant with experience at Meta and Stitch Fix. At Stitch Fix, Jason developed impactful AI systems, like a $50 million product similarity search and the widely adopted Flight recommendation framework. Now, he helps startups and enterprises design and deploy production-level AI applications, with a focus on retrieval-augmented generation (RAG) and scalable solutions. This episode is a bit of an experiment. Instead of our usual technical deep dives, we’re focusing on the world of AI consulting and freelancing. We explore Jason’s consulting playbook, covering how he structures contracts to maximize value, strategies for moving from hourly billing to securing larger deals, and the mindset shift needed to align incentives with clients. We’ll also discuss the challenges of moving from deterministic software to probabilistic AI systems and even do a live role-playing session where Jason coaches me on client engagement and pricing pitfalls. LINKS The livestream on YouTube Jason's Upcoming course: AI Consultant Accelerator: From Expert to High-Demand Business Hugo's upcoming course: Building LLM Applications for Data Scientists and Software Engineers Jason's website Jason's indie consulting newsletter Your AI Product Needs Evals by Hamel Husain What We’ve Learned From A Year of Building with LLMs Dear Future AI Consultant by Jason Alex Hormozi's books The Burnout Society by Byung-Chul Han Jason on Twitter Vanishing Gradients on Twitter Hugo on Twitter Vanishing Gradients' lu.ma calendar Vanishing Gradients on YouTube…

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Episode 37: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 2 50:36

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Hugo speaks with three leading figures from the world of AI research: Sander Schulhoff, a recent University of Maryland graduate and lead contributor to the Learn Prompting initiative; Philip Resnik, professor at the University of Maryland, known for his pioneering work in computational linguistics; and Dennis Peskoff, a researcher from Princeton specializing in prompt engineering and its applications in the social sciences. This is Part 2 of a special two-part episode, prompted—no pun intended—by these guys being part of a team, led by Sander, that wrote a 76-page survey analyzing prompting techniques, agents, and generative AI. The survey included contributors from OpenAI, Microsoft, the University of Maryland, Princeton, and more. In this episode, we cover: The Prompt Report: A comprehensive survey on prompting techniques, agents, and generative AI, including advanced evaluation methods for assessing these techniques. Security Risks and Prompt Hacking: A detailed exploration of the security concerns surrounding prompt engineering, including Sander’s thoughts on its potential applications in cybersecurity and military contexts. AI’s Impact Across Fields: A discussion on how generative AI is reshaping various domains, including the social sciences and security. Multimodal AI: Updates on how large language models (LLMs) are expanding to interact with images, code, and music. Case Study - Detecting Suicide Risk: A careful examination of how prompting techniques are being used in important areas like detecting suicide risk, showcasing the critical potential of AI in addressing sensitive, real-world challenges. The episode concludes with a reflection on the evolving landscape of LLMs and multimodal AI, and what might be on the horizon. If you haven’t yet, make sure to check out Part 1 , where we discuss the history of NLP, prompt engineering techniques, and Sander’s development of the Learn Prompting initiative. LINKS The livestream on YouTube The Prompt Report: A Systematic Survey of Prompting Techniques Learn Prompting: Your Guide to Communicating with AI Vanishing Gradients on Twitter Hugo on Twitter Vanishing Gradients' lu.ma calendar Vanishing Gradients on YouTube…

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Episode 36: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 1 1:03:46

for 30 weeks siden1:03:46

1:03:46

Hugo speaks with three leading figures from the world of AI research: Sander Schulhoff, a recent University of Maryland graduate and lead contributor to the Learn Prompting initiative; Philip Resnik, professor at the University of Maryland, known for his pioneering work in computational linguistics; and Dennis Peskoff, a researcher from Princeton specializing in prompt engineering and its applications in the social sciences. This is Part 1 of a special two-part episode, prompted—no pun intended—by these guys being part of a team, led by Sander, that wrote a 76-page survey analyzing prompting techniques, agents, and generative AI. The survey included contributors from OpenAI, Microsoft, the University of Maryland, Princeton, and more. In this first part, we’ll explore the critical role of prompt engineering, & diving into adversarial techniques like prompt hacking and the challenges of evaluating these techniques. we’ll examine the impact of few-shot learning and the groundbreaking taxonomy of prompting techniques from the Prompt Report. Along the way, we’ll uncover the rich history of natural language processing (NLP) and AI, showing how modern prompting techniques evolved from early rule-based systems and statistical methods. we’ll also hear how Sander’s experimentation with GPT-3 for diplomatic tasks led him to develop Learn Prompting, and how Dennis highlights the accessibility of AI through prompting, which allows non-technical users to interact with AI without needing to code. Finally, we’ll explore the future of multimodal AI, where LLMs interact with images, code, and even music creation. Make sure to tune in to Part 2, where we dive deeper into security risks, prompt hacking, and more. LINKS The livestream on YouTube The Prompt Report: A Systematic Survey of Prompting Techniques Learn Prompting: Your Guide to Communicating with AI Vanishing Gradients on Twitter Hugo on Twitter Vanishing Gradients' lu.ma calendar Vanishing Gradients on YouTube…

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Episode 35: Open Science at NASA -- Measuring Impact and the Future of AI 58:13

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Hugo speaks with Dr. Chelle Gentemann, Open Science Program Scientist for NASA’s Office of the Chief Science Data Officer, about NASA’s ambitious efforts to integrate AI across the research lifecycle. In this episode, we’ll dive deeper into how AI is transforming NASA’s approach to science, making data more accessible and advancing open science practices. We explore Measuring the Impact of Open Science: How NASA is developing new metrics to evaluate the effectiveness of open science, moving beyond traditional publication-based assessments. The Process of Scientific Discovery: Insights into the collaborative nature of research and how breakthroughs are achieved at NASA. ** AI Applications in NASA’s Science:** From rats in space to exploring the origins of the universe, we cover how AI is being applied across NASA’s divisions to improve data accessibility and analysis. Addressing Challenges in Open Science: The complexities of implementing open science within government agencies and research environments. Reforming Incentive Systems: How NASA is reconsidering traditional metrics like publications and citations, and starting to recognize contributions such as software development and data sharing. The Future of Open Science: How open science is shaping the future of research, fostering interdisciplinary collaboration, and increasing accessibility. This conversation offers valuable insights for researchers, data scientists, and those interested in the practical applications of AI and open science. Join us as we discuss how NASA is working to make science more collaborative, reproducible, and impactful. LINKS The livestream on YouTube NASA's Open Science 101 course <-- do it to learn and also to get NASA Swag! Science Cast NASA and IBM Openly Release Geospatial AI Foundation Model for NASA Earth Observation Data Jake VanderPlas' daily conundrum tweet from 2013 Replit, "an AI-powered software development & deployment platform for building, sharing, and shipping software fast."…

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Episode 34: The AI Revolution Will Not Be Monopolized 1:42:51

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Hugo speaks with Ines Montani and Matthew Honnibal, the creators of spaCy and founders of Explosion AI. Collectively, they've had a huge impact on the fields of industrial natural language processing (NLP), ML, and AI through their widely-used open-source library spaCy and their innovative annotation tool Prodigy. These tools have become essential for many data scientists and NLP practitioners in industry and academia alike. In this wide-ranging discussion, we dive into: • The evolution of applied NLP and its role in industry • The balance between large language models and smaller, specialized models • Human-in-the-loop distillation for creating faster, more data-private AI systems • The challenges and opportunities in NLP, including modularity, transparency, and privacy • The future of AI and software development • The potential impact of AI regulation on innovation and competition We also touch on their recent transition back to a smaller, more independent-minded company structure and the lessons learned from their journey in the AI startup world. Ines and Matt offer invaluable insights for data scientists, machine learning practitioners, and anyone interested in the practical applications of AI. They share their thoughts on how to approach NLP projects, the importance of data quality, and the role of open-source in advancing the field. Whether you're a seasoned NLP practitioner or just getting started with AI, this episode offers a wealth of knowledge from two of the field's most respected figures. Join us for a discussion that explores the current landscape of AI development, with insights that bridge the gap between cutting-edge research and real-world applications. LINKS The livestream on YouTube How S&P Global is making markets more transparent with NLP, spaCy and Prodigy A practical guide to human-in-the-loop distillation Laws of Tech: Commoditize Your Complement spaCy: Industrial-Strength Natural Language Processing LLMs with spaCy Explosion, building developer tools for AI, Machine Learning and Natural Language Processing Back to our roots: Company update and future plans, by Matt and Ines Matt's detailed blog post: back to our roots Ines on twitter Matt on twitter Vanishing Gradients on Twitter Hugo on Twitter Check out and subcribe to our lu.ma calendar for upcoming livestreams!…

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Episode 33: What We Learned Teaching LLMs to 1,000s of Data Scientists 1:25:10

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Hugo speaks with Dan Becker and Hamel Husain, two veterans in the world of data science, machine learning, and AI education. Collectively, they’ve worked at Google, DataRobot, Airbnb, Github (where Hamel built out the precursor to copilot and more) and they both currently work as independent LLM and Generative AI consultants. Dan and Hamel recently taught a course on fine-tuning large language models that evolved into a full-fledged conference, attracting over 2,000 participants. This experience gave them unique insights into the current state and future of AI education and application. In this episode, we dive into: The evolution of their course from fine-tuning to a comprehensive AI conference The unexpected challenges and insights gained from teaching LLMs to data scientists The current state of AI tooling and accessibility compared to a decade ago The role of playful experimentation in driving innovation in the field Thoughts on the economic impact and ROI of generative AI in various industries The importance of proper evaluation in machine learning projects Future predictions for AI education and application in the next five years We also touch on the challenges of using AI tools effectively, the potential for AI in physical world applications, and the need for a more nuanced understanding of AI capabilities in the workplace. During our conversation, Dan mentions an exciting project he's been working on, which we couldn't showcase live due to technical difficulties. However, I've included a link to a video demonstration in the show notes that you won't want to miss. In this demo, Dan showcases his innovative AI-powered 3D modeling tool that allows users to create 3D printable objects simply by describing them in natural language. LINKS The livestream on YouTube Educational resources from Dan and Hamel's LLM course Upwork Study Finds Employee Workloads Rising Despite Increased C-Suite Investment in Artificial Intelligence Episode 29: Lessons from a Year of Building with LLMs (Part 1) Episode 30: Lessons from a Year of Building with LLMs (Part 2) Dan's demo: Creating Physical Products with Generative AI Build Great AI, Dan's boutique consulting firm helping clients be successful with large language models Parlance Labs, Hamel's Practical consulting that improves your AI Hamel on Twitter Dan on Twitter Vanishing Gradients on Twitter Hugo on Twitter…

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Episode 32: Building Reliable and Robust ML/AI Pipelines 1:15:10

for 39 weeks siden1:15:10

1:15:10

Hugo speaks with Shreya Shankar, a researcher at UC Berkeley focusing on data management systems with a human-centered approach. Shreya's work is at the cutting edge of human-computer interaction (HCI) and AI, particularly in the realm of large language models (LLMs). Her impressive background includes being the first ML engineer at Viaduct, doing research engineering at Google Brain, and software engineering at Facebook. In this episode, we dive deep into the world of LLMs and the critical challenges of building reliable AI pipelines. We'll explore: The fascinating journey from classic machine learning to the current LLM revolution Why Shreya believes most ML problems are actually data management issues The concept of "data flywheels" for LLM applications and how to implement them The intriguing world of evaluating AI systems - who validates the validators? Shreya's work on SPADE and EvalGen, innovative tools for synthesizing data quality assertions and aligning LLM evaluations with human preferences The importance of human-in-the-loop processes in AI development The future of low-code and no-code tools in the AI landscape We'll also touch on the potential pitfalls of over-relying on LLMs, the concept of "Habsburg AI," and how to avoid disappearing up our own proverbial arseholes in the world of recursive AI processes. Whether you're a seasoned AI practitioner, a curious data scientist, or someone interested in the human side of AI development, this conversation offers valuable insights into building more robust, reliable, and human-centered AI systems. LINKS The livestream on YouTube Shreya's website Shreya on Twitter Data Flywheels for LLM Applications SPADE: Synthesizing Data Quality Assertions for Large Language Model Pipelines What We’ve Learned From A Year of Building with LLMs Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences Operationalizing Machine Learning: An Interview Study Vanishing Gradients on Twitter Hugo on Twitter In the podcast, Hugo also mentioned that this was the 5th time he and Shreya chatted publicly. which is wild! If you want to dive deep into Shreya's work and related topics through their chats, you can check them all out here: Outerbounds' Fireside Chat: Operationalizing ML -- Patterns and Pain Points from MLOps Practitioners The Past, Present, and Future of Generative AI LLMs, OpenAI Dev Day, and the Existential Crisis for Machine Learning Engineering Lessons from a Year of Building with LLMs Check out and subcribe to our lu.ma calendar for upcoming livestreams!…

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Episode 31: Rethinking Data Science, Machine Learning, and AI 1:36:04

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Hugo speaks with Vincent Warmerdam, a senior data professional and machine learning engineer at :probabl, the exclusive brand operator of scikit-learn. Vincent is known for challenging common assumptions and exploring innovative approaches in data science and machine learning. In this episode, they dive deep into rethinking established methods in data science, machine learning, and AI. We explore Vincent's principled approach to the field, including: The critical importance of exposing yourself to real-world problems before applying ML solutions Framing problems correctly and understanding the data generating process The power of visualization and human intuition in data analysis Questioning whether algorithms truly meet the actual problem at hand The value of simple, interpretable models and when to consider more complex approaches The importance of UI and user experience in data science tools Strategies for preventing algorithmic failures by rethinking evaluation metrics and data quality The potential and limitations of LLMs in the current data science landscape The benefits of open-source collaboration and knowledge sharing in the community Throughout the conversation, Vincent illustrates these principles with vivid, real-world examples from his extensive experience in the field. They also discuss Vincent's thoughts on the future of data science and his call to action for more knowledge sharing in the community through blogging and open dialogue. LINKS The livestream on YouTube Vincent's blog CalmCode scikit-lego Vincent's book Data Science Fiction (WIP) The Deon Checklist, an ethics checklist for data scientists Of oaths and checklists, by DJ Patil, Hilary Mason and Mike Loukides Vincent's Getting Started with NLP and spaCy Course course on Talk Python Vincent on twitter :probabl. on twitter Vincent's PyData Amsterdam Keynote "Natural Intelligence is All You Need [tm]" Vincent's PyData Amsterdam 2019 talk: The profession of solving (the wrong problem) Vanishing Gradients on Twitter Hugo on Twitter Check out and subcribe to our lu.ma calendar for upcoming livestreams!…

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Episode 30: Lessons from a Year of Building with LLMs (Part 2) 1:15:23

for 44 weeks siden1:15:23

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Hugo speaks about Lessons Learned from a Year of Building with LLMs with Eugene Yan from Amazon, Bryan Bischof from Hex, Charles Frye from Modal, Hamel Husain from Parlance Labs, and Shreya Shankar from UC Berkeley. These five guests, along with Jason Liu who couldn't join us, have spent the past year building real-world applications with Large Language Models (LLMs). They've distilled their experiences into a report of 42 lessons across operational, strategic, and tactical dimensions , and they're here to share their insights. We’ve split this roundtable into 2 episodes and, in this second episode, we'll explore: An inside look at building end-to-end systems with LLMs; The experimentation mindset: Why it's the key to successful AI products; Building trust in AI: Strategies for getting stakeholders on board; The art of data examination: Why looking at your data is more crucial than ever; Evaluation strategies that separate the pros from the amateurs. Although we're focusing on LLMs, many of these insights apply broadly to data science, machine learning, and product development, more generally. LINKS The livestream on YouTube The Report: What We’ve Learned From A Year of Building with LLMs About the Guests/Authors <-- connect with them all on LinkedIn, follow them on Twitter, subscribe to their newsletters! (Seriously, though, the amount of collective wisdom here is 🤑 Your AI product needs evals by Hamel Husain Prompting Fundamentals and How to Apply them Effectively by Eugene Yan Fuck You, Show Me The Prompt by Hamel Husain Vanishing Gradients on YouTube Vanishing Gradients on Twitter Vanishing Gradients on Lu.ma…

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Episode 29: Lessons from a Year of Building with LLMs (Part 1) 1:30:21

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1:30:21

Hugo speaks about Lessons Learned from a Year of Building with LLMs with Eugene Yan from Amazon, Bryan Bischof from Hex, Charles Frye from Modal, Hamel Husain from Parlance Labs, and Shreya Shankar from UC Berkeley. These five guests, along with Jason Liu who couldn't join us, have spent the past year building real-world applications with Large Language Models (LLMs). They've distilled their experiences into a report of 42 lessons across operational, strategic, and tactical dimensions , and they're here to share their insights. We’ve split this roundtable into 2 episodes and, in this first episode, we'll explore: The critical role of evaluation and monitoring in LLM applications and why they're non-negotiable, including "evals" - short for evaluations, which are automated tests for assessing LLM performance and output quality; Why data literacy is your secret weapon in the AI landscape; The fine-tuning dilemma: when to do it and when to skip it; Real-world lessons from building LLM applications that textbooks won't teach you; The evolving role of data scientists and AI engineers in the age of AI. Although we're focusing on LLMs, many of these insights apply broadly to data science, machine learning, and product development, more generally. LINKS The livestream on YouTube The Report: What We’ve Learned From A Year of Building with LLMs About the Guests/Authors <-- connect with them all on LinkedIn, follow them on Twitter, subscribe to their newsletters! (Seriously, though, the amount of collective wisdom here is 🤑 Your AI product needs evals by Hamel Husain Prompting Fundamentals and How to Apply them Effectively by Eugene Yan Fuck You, Show Me The Prompt by Hamel Husain Vanishing Gradients on YouTube Vanishing Gradients on Twitter Vanishing Gradients on Lu.ma…

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Episode 28: Beyond Supervised Learning: The Rise of In-Context Learning with LLMs 1:05:38

for 46 weeks siden1:05:38

1:05:38

Hugo speaks with Alan Nichol, co-founder and CTO of Rasa, where they build software to enable developers to create enterprise-grade conversational AI and chatbot systems across industries like telcos, healthcare, fintech, and government. What's super cool is that Alan and the Rasa team have been doing this type of thing for over a decade, giving them a wealth of wisdom on how to effectively incorporate LLMs into chatbots - and how not to. For example, if you want a chatbot that takes specific and important actions like transferring money, do you want to fully entrust the conversation to one big LLM like ChatGPT, or secure what the LLMs can do inside key business logic? In this episode, they also dive into the history of conversational AI and explore how the advent of LLMs is reshaping the field. Alan shares his perspective on how supervised learning has failed us in some ways and discusses what he sees as the most overrated and underrated aspects of LLMs. Alan offers advice for those looking to work with LLMs and conversational AI, emphasizing the importance of not sleeping on proven techniques and looking beyond the latest hype. In a live demo, he showcases Rasa's Calm (Conversational AI with Language Models), which allows developers to define business logic declaratively and separate it from the LLM, enabling reliable execution of conversational flows. LINKS The livestream on YouTube Alan's Rasa CALM Demo: Building Conversational AI with LLMs Alan on twitter.com Rasa CALM, an LLM-native approach to building reliable conversational AI Task-Oriented Dialogue with In-Context Learning 'We don’t know how to build conversational software yet' by Alan Nicol Vanishing Gradients on Twitter Hugo on Twitter Upcoming Livestreams Lessons from a Year of Building with LLMs VALIDATING THE VALIDATORS with Shreya Shanker…

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Episode 27: How to Build Terrible AI Systems 1:32:24

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1:32:24

Hugo speaks with Jason Liu, an independent consultant who uses his expertise in recommendation systems to help fast-growing startups build out their RAG applications. He was previously at Meta and Stitch Fix is also the creator of Instructor, Flight, and an ML and data science educator. They talk about how Jason approaches consulting companies across many industries, including construction and sales, in building production LLM apps, his playbook for getting ML and AI up and running to build and maintain such apps, and the future of tooling to do so. They take an inverted thinking approach, envisaging all the failure modes that would result in building terrible AI systems, and then figure out how to avoid such pitfalls. LINKS The livestream on YouTube Jason's website PyDdantic is all you need, Jason's Keynote at AI Engineer Summit, 2023 How to build a terrible RAG system by Jason To express interest in Jason's Systematically improving RAG Applications course Vanishing Gradients on Twitter Hugo on Twitter Upcoming Livestreams Good Riddance to Supervised Learning with Alan Nichol (CTO and co-founder, Rasa) Lessons from a Year of Building with LLMs…

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Episode 26: Developing and Training LLMs From Scratch 1:51:35

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1:51:35

Hugo speaks with Sebastian Raschka, a machine learning & AI researcher, programmer, and author. As Staff Research Engineer at Lightning AI, he focuses on the intersection of AI research, software development, and large language models (LLMs). How do you build LLMs? How can you use them, both in prototype and production settings? What are the building blocks you need to know about? In this episode, we’ll tell you everything you need to know about LLMs, but were too afraid to ask: from covering the entire LLM lifecycle, what type of skills you need to work with them, what type of resources and hardware, prompt engineering vs fine-tuning vs RAG, how to build an LLM from scratch, and much more. The idea here is not that you’ll need to use an LLM you’ve built from scratch, but that we’ll learn a lot about LLMs and how to use them in the process. Near the end we also did some live coding to fine-tune GPT-2 in order to create a spam classifier! LINKS The livestream on YouTube Sebastian's website Machine Learning Q and AI: 30 Essential Questions and Answers on Machine Learning and AI by Sebastian Build a Large Language Model (From Scratch) by Sebastian PyTorch Lightning Lightning Fabric LitGPT Sebastian's notebook for finetuning GPT-2 for spam classification! The end of fine-tuning: Jeremy Howard on the Latent Space Podcast Our next livestream: How to Build Terrible AI Systems with Jason Liu Vanishing Gradients on Twitter Hugo on Twitter…

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Episode 25: Fully Reproducible ML & AI Workflows 1:20:38

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1:20:38

Hugo speaks with Omoju Miller, a machine learning guru and founder and CEO of Fimio, where she is building 21st century dev tooling. In the past, she was Technical Advisor to the CEO at GitHub, spent time co-leading non-profit investment in Computer Science Education for Google, and served as a volunteer advisor to the Obama administration’s White House Presidential Innovation Fellows. We need open tools, open data, provenance, and the ability to build fully reproducible, transparent machine learning workflows. With the advent of closed-source, vendor-based APIs and compute becoming a form of gate-keeping, developer tools are at the risk of becoming commoditized and developers becoming consumers. We’ll talk about how ideas for escaping these burgeoning walled gardens. We’ll dive into What fully reproducible ML workflows would look like, including git for the workflow build process, The need for loosely coupled and composable tools that embrace a UNIX-like philosophy, What a much more scientific toolchain would look like, What a future open sources commons for Generative AI could look like, What an open compute ecosystem could look like, How to create LLMs and tooling so everyone can use them to build production-ready apps, And much more! LINKS The livestream on YouTube Omoju on Twitter Hugo on Twitter Vanishing Gradients on Twitter Lu.ma Calendar that includes details of Hugo's European Tour for Outerbounds Blog post that includes details of Hugo's European Tour for Outerbounds…

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Episode 24: LLM and GenAI Accessibility 1:30:03

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1:30:03

Hugo speaks with Johno Whitaker, a Data Scientist/AI Researcher doing R&D with answer.ai. His current focus is on generative AI, flitting between different modalities. He also likes teaching and making courses, having worked with both Hugging Face and fast.ai in these capacities. Johno recently reminded Hugo how hard everything was 10 years ago: “Want to install TensorFlow? Good luck. Need data? Perhaps try ImageNet. But now you can use big models from Hugging Face with hi-res satellite data and do all of this in a Colab notebook. Or think ecology and vision models… or medicine and multimodal models!” We talk about where we’ve come from regarding tooling and accessibility for foundation models, ML, and AI, where we are, and where we’re going. We’ll delve into What the Generative AI mindset is, in terms of using atomic building blocks, and how it evolved from both the data science and ML mindsets; How fast.ai democratized access to deep learning, what successes they had, and what was learned; The moving parts now required to make GenAI and ML as accessible as possible; The importance of focusing on UX and the application in the world of generative AI and foundation models; The skillset and toolkit needed to be an LLM and AI guru; What they’re up to at answer.ai to democratize LLMs and foundation models. LINKS The livestream on YouTube Zindi, the largest professional network for data scientists in Africa A new old kind of R&D lab: Announcing Answer.AI Why and how I’m shifting focus to LLMs by Johno Whitaker Applying AI to Immune Cell Networks by Rachel Thomas Replicate -- a cool place to explore GenAI models, among other things Hands-On Generative AI with Transformers and Diffusion Models Johno on Twitter Hugo on Twitter Vanishing Gradients on Twitter SciPy 2024 CFP Escaping Generative AI Walled Gardens with Omoju Miller, a Vanishing Gradients Livestream…

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Episode 23: Statistical and Algorithmic Thinking in the AI Age 1:20:37

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1:20:37

Hugo speaks with Allen Downey, a curriculum designer at Brilliant, Professor Emeritus at Olin College, and the author of Think Python, Think Bayes, Think Stats, and other computer science and data science books. In 2019-20 he was a Visiting Professor at Harvard University. He previously taught at Wellesley College and Colby College and was a Visiting Scientist at Google. He is also the author of the upcoming book Probably Overthinking It! They discuss Allen's new book and the key statistical and data skills we all need to navigate an increasingly data-driven and algorithmic world. The goal was to dive deep into the statistical paradoxes and fallacies that get in the way of using data to make informed decisions. For example, when it was reported in 2021 that “in the United Kingdom, 70-plus percent of the people who die now from COVID are fully vaccinated,” this was correct but the implication was entirely wrong. Their conversation jumps into many such concrete examples to get to the bottom of using data for more than “lies, damned lies, and statistics.” They cover Information and misinformation around pandemics and the base rate fallacy; The tools we need to comprehend the small probabilities of high-risk events such as stock market crashes, earthquakes, and more; The many definitions of algorithmic fairness, why they can't all be met at once, and what we can do about it; Public health, the need for robust causal inference, and variations on Berkson’s paradox, such as the low-birthweight paradox: an influential paper found that that the mortality rate for children of smokers is lower for low-birthweight babies; Why none of us are normal in any sense of the word, both in physical and psychological measurements; The Inspection paradox, which shows up in the criminal justice system and distorts our perception of prison sentences and the risk of repeat offenders. LINKS The livestream on YouTube Allen Downey on Github Allen's new book Probably Overthinking It! Allen on Twitter Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions by Mitchell et al.…

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Episode 22: LLMs, OpenAI, and the Existential Crisis for Machine Learning Engineering 1:20:07

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1:20:07

Jeremy Howard (Fast.ai), Shreya Shankar (UC Berkeley), and Hamel Husain (Parlance Labs) join Hugo Bowne-Anderson to talk about how LLMs and OpenAI are changing the worlds of data science, machine learning, and machine learning engineering. Jeremy Howard is co-founder of fast.ai, an ex-Chief Scientist at Kaggle, and creator of the ULMFiT approach on which all modern language models are based. Shreya Shankar is at UC Berkeley, ex Google brain, Facebook, and Viaduct. Hamel Husain has his own generative AI and LLM consultancy Parlance Labs and was previously at Outerbounds, Github, and Airbnb. They talk about How LLMs shift the nature of the work we do in DS and ML, How they change the tools we use, The ways in which they could displace the role of traditional ML (e.g. will we stop using xgboost any time soon?), How to navigate all the new tools and techniques, The trade-offs between open and closed models, Reactions to the recent Open Developer Day and the increasing existential crisis for ML. LINKS The panel on YouTube Hugo and Jeremy's upcoming livestream on what the hell happened recently at OpenAI, among many other things Vanishing Gradients on YouTube Vanishing Gradients on twitter…

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Episode 21: Deploying LLMs in Production: Lessons Learned 1:08:11

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1:08:11

Hugo speaks with Hamel Husain, a machine learning engineer who loves building machine learning infrastructure and tools 👷. Hamel leads and contributes to many popular open-source machine learning projects. He also has extensive experience (20+ years) as a machine learning engineer across various industries, including large tech companies like Airbnb and GitHub. At GitHub, he led CodeSearchNet , a large language model for semantic search that was a precursor to CoPilot. Hamel is the founder of Parlance-Labs , a research and consultancy focused on LLMs. They talk about generative AI, large language models, the business value they can generate, and how to get started. They delve into Where Hamel is seeing the most business interest in LLMs (spoiler: the answer isn’t only tech); Common misconceptions about LLMs; The skills you need to work with LLMs and GenAI models; Tools and techniques, such as fine-tuning, RAGs, LoRA, hardware, and more! Vendor APIs vs OSS models. LINKS Our upcoming livestream LLMs, OpenAI Dev Day, and the Existential Crisis for Machine Learning Engineering with Jeremy Howard (Fast.ai), Shreya Shankar (UC Berkeley), and Hamel Husain (Parlance Labs): Sign up for free! Our recent livestream Data and DevOps Tools for Evaluating and Productionizing LLMs with Hamel and Emil Sedgh, Lead AI engineer at Rechat -- in it, we showcase an actual industrial use case that Hamel and Emil are working on with Rechat, a real estate CRM, taking you through LLM workflows and tools. Extended Guide: Instruction-tune Llama 2 by Philipp Schmid The livestream recoding of this episode! Hamel on twitter…

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Episode 20: Data Science: Past, Present, and Future 1:26:39

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1:26:39

Hugo speaks with Chris Wiggins (Columbia, NYTimes) and Matthew Jones (Princeton) about their recent book How Data Happened, and the Columbia course it expands upon, data: past, present, and future. Chris is an associate professor of applied mathematics at Columbia University and the New York Times’ chief data scientist, and Matthew is a professor of history at Princeton University and former Guggenheim Fellow. From facial recognition to automated decision systems that inform who gets loans and who receives bail, we all now move through a world determined by data-empowered algorithms. These technologies didn’t just appear: they are part of a history that goes back centuries, from the census enshrined in the US Constitution to the birth of eugenics in Victorian Britain to the development of Google search. DJ Patil, former U.S. Chief Data Scientist, said of the book "This is the first comprehensive look at the history of data and how power has played a critical role in shaping the history. It’s a must read for any data scientist about how we got here and what we need to do to ensure that data works for everyone." If you’re a data scientist, machine learning engineer, or work with data in any way, it’s increasingly important to know more about the history and future of the work that you do and understand how your work impacts society and the world. Among other things, they'll delve into the history of human use of data; how data are used to reveal insight and support decisions; how data and data-powered algorithms shape, constrain, and manipulate our commercial, civic, and personal transactions and experiences; and how exploration and analysis of data have become part of our logic and rhetoric of communication and persuasion. You can also sign up for our next livestreamed podcast recording here ! LINKS How Data Happened, the book! data: past, present, and future, the course Race After Technology, by Ruha Benjamin The problem with metrics is a big problem for AI by Rachel Thomas Vanishing Gradients on YouTube…

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Episode 19: Privacy and Security in Data Science and Machine Learning 1:23:19

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Hugo speaks with Katharine Jarmul about privacy and security in data science and machine learning. Katharine is a Principal Data Scientist at Thoughtworks Germany focusing on privacy, ethics, and security for data science workflows. Previously, she has held numerous roles at large companies and startups in the US and Germany, implementing data processing and machine learning systems with a focus on reliability, testability, privacy, and security. In this episode, Hugo and Katharine talk about What data privacy and security are, what they aren’t and the differences between them (hopefully dispelling common misconceptions along the way!); Why you should care about them (hint: the answers will involve regulatory, ethical, risk, and organizational concerns); Data governance, anonymization techniques, and privacy in data pipelines; Privacy attacks! The state of the art in privacy-aware machine learning and data science, including federated learning; What you need to know about the current state of regulation, including GDPR and CCPA… And much more, all the while grounding our conversation in real-world examples from data science, machine learning, business, and life! You can also sign up for our next livestreamed podcast recording here ! LINKS Win a copy of Practical Data Privacy, Katharine's new book! Katharine on twitter Vanishing Gradients on YouTube Probably Private, a newsletter for privacy and data science enthusiasts Probably Private on YouTube…

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Episode 18: Research Data Science in Biotech 1:12:42

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Hugo speaks with Eric Ma about Research Data Science in Biotech. Eric leads the Research team in the Data Science and Artificial Intelligence group at Moderna Therapeutics. Prior to that, he was part of a special ops data science team at the Novartis Institutes for Biomedical Research's Informatics department. In this episode, Hugo and Eric talk about What tools and techniques they use for drug discovery (such as mRNA vaccines and medicines); The importance of machine learning, deep learning, and Bayesian inference; How to think more generally about such high-dimensional, multi-objective optimization problems; The importance of open-source software and Python; Institutional and cultural questions, including hiring and the trade-offs between being an individual contributor and a manager; How they’re approaching accelerating discovery science to the speed of thought using computation, data science, statistics, and ML. And as always, much, much more! LINKS Eric's website Eric on twitter Vanishing Gradients on YouTube Cell Biology by the Numbers by Ron Milo and Rob Phillips Eric's JAX tutorials at PyCon and SciPy Eric's blog post on Hiring data scientists at Moderna!…

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Episode 17: End-to-End Data Science 1:16:04

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Hugo speaks with Tanya Cashorali, a data scientist and consultant that helps businesses get the most out of data, about what end-to-end data science looks like across many industries, such as retail, defense, biotech, and sports, including scoping out projects, figuring out the correct questions to ask, how projects can change, delivering on the promise, the importance of rapid prototyping, what it means to put models in production, and how to measure success. And much more, all the while grounding their conversation in real-world examples from data science, business, and life. In a world where most organizations think they need AI and yet 10-15% of data science actually involves model building, it’s time to get real about how data science and machine learning actually deliver value! LINKS Tanya on Twitter Vanishing Gradients on YouTube Saving millions with a Shiny app | Data Science Hangout with Tanya Cashorali Our next livestream: Research Data Science in Biotech with Eric Ma…

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Episode 16: Data Science and Decision Making Under Uncertainty 1:23:15

for 2 years siden1:23:15

1:23:15

Hugo speaks with JD Long, agricultural economist, quant, and stochastic modeler, about decision making under uncertainty and how we can use our knowledge of risk, uncertainty, probabilistic thinking, causal inference, and more to help us use data science and machine learning to make better decisions in an uncertain world. This is part 2 of a two part conversation in which we delve into decision making under uncertainty. Feel free to check out part 1 here but this episode should also stand alone. Why am I speaking to JD about all of this? Because not only is he a wild conversationalist with a real knack for explaining hard to grok concepts with illustrative examples and useful stories, but he has worked for many years in re-insurance, that’s right, not insurance but re-insurance – these are the people who insure the insurers so if anyone can actually tell us about risk and uncertainty in decision making, it’s him! In part 1, we discussed risk, uncertainty, probabilistic thinking, and simulation, all with a view towards improving decision making. In this, part 2, we discuss the ins and outs of decision making under uncertainty, including How data science can be more tightly coupled with the decision function in organisations; Some common mistakes and failure modes of making decisions under uncertainty; Heuristics for principled decision-making in data science; The intersection of model building, storytelling, and cognitive biases to keep in mind; As JD says, and I paraphrase, “You may think you train your models, but your models are really training you.” Links Vanishing Gradients' new YouTube channel! JD on twitter Executive Data Science, episode 5 of Vanishing Gradients, in which Jim Savage and Hugo talk through decision making and why you should always be integrating your loss function over your posterior Fooled by Randomness by Nassim Taleb Superforecasting: The Art and Science of Prediction Philip E. Tetlock and Dan Gardner Thinking in Bets by Annie Duke The Signal and the Noise: Why So Many Predictions Fail by Nate Silver Thinking, Fast and Slow by Daniel Kahneman…

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