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PODCAST

Vanishing Gradients

Hugo Bowne-Anderson

A podcast about all things data, brought to you by data scientist Hugo Bowne-Anderson. It"s time for more critical conversations about the challenges in our industry in order to build better compasses for the solution space! To this end, this podcast will consist of long-format conversations between Hugo and other people who work broadly in the data science, machine learning, and AI spaces. We"ll dive deep into all the moving parts of the data world, so if you"re new to the space, you"ll have an

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All Episodes

59:04

Episode 62: Practical AI at Work: How Execs and...

Vanishing Gradients ·

2025/10/31

Many leaders are trapped between chasing ambitious, ill-defined AI projects and the paralysis of not knowing where to start. Dr. Randall Olson argues that the real opportunity isn't in moonshots, but in the "trillions of dollars of business value" available right now. As co-founder of Wyrd Studios, he bridges the gap between data science, AI engineering, and executive strategy to deliver a practical framework for execution. In this episode, Randy and Hugo lay out how to find and solve what might be

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28:04

Episode 61: The AI Agent Reliability Cliff: What...

Vanishing Gradients ·

2025/10/16

Most AI teams find their multi-agent systems devolving into chaos, but ML Engineer Alex Strick van Linschoten argues they are ignoring the production reality. In this episode, he draws on insights from the LLM Ops Database (750+ real-world deployments then; now nearly 1,000!) to systematically measure and engineer constraint, turning unreliable prototypes into robust, enterprise-ready AI. Drawing from his work at Zen ML, Alex details why success requires scaling down and enforcing MLOps discipline

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1:13:15

Episode 60: 10 Things I Hate About AI Evals with...

Vanishing Gradients ·

2025/09/30

Most AI teams find "evals" frustrating, but ML Engineer Hamel Husain argues they’re just using the wrong playbook. In this episode, he lays out a data-centric approach to systematically measure and improve AI, turning unreliable prototypes into robust, production-ready systems. Drawing from his experience getting countless teams unstuck, Hamel explains why the solution requires a "revenge of the data scientists." He details the essential mindset shifts, error analysis techniques, and practical steps

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47:37

Episode 59: Patterns and Anti-Patterns For Building...

Vanishing Gradients ·

2025/09/23

John Berryman (Arcturus Labs; early GitHub Copilot engineer; co-author of Relevant Search and Prompt Engineering for LLMs) has spent years figuring out what makes AI applications actually work in production. In this episode, he shares the “seven deadly sins” of LLM development — and the practical fixes that keep projects from stalling. From context management to retrieval debugging, John explains the patterns he’s seen succeed, the mistakes to avoid, and why it helps to think of an LLM as an “AI

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1:00:45

Episode 58: Building GenAI Systems That Make Business...

Vanishing Gradients ·

2025/09/09

While most conversations about generative AI focus on chatbots, Thomas Wiecki (PyMC Labs, PyMC) has been building systems that help companies make actual business decisions. In this episode, he shares how Bayesian modeling and synthetic consumers can be combined with LLMs to simulate customer reactions, guide marketing spend, and support strategy. Drawing from his work with Colgate and others, Thomas explains how to scale survey methods with AI, where agents fit into analytics workflows, and what

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41:27

Episode 57: AI Agents and LLM Judges at Scale:...

Vanishing Gradients ·

2025/08/29

While many people talk about “agents,” Shreya Shankar (UC Berkeley) has been building the systems that make them reliable. In this episode, she shares how AI agents and LLM judges can be used to process millions of documents accurately and cheaply. Drawing from work on projects ranging from databases of police misconduct reports to large-scale customer transcripts, Shreya explains the frameworks, error analysis, and guardrails needed to turn flaky LLM outputs into trustworthy pipelines. We talk

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45:40

Episode 56: DeepMind Just Dropped Gemma 270M... And...

Vanishing Gradients ·

2025/08/14

While much of the AI world chases ever-larger models, Ravin Kumar (Google DeepMind) and his team build across the size spectrum, from billions of parameters down to this week’s release: Gemma 270M, the smallest member yet of the Gemma 3 open-weight family. At just 270 million parameters, a quarter the size of Gemma 1B, it’s designed for speed, efficiency, and fine-tuning. We explore what makes 270M special, where it fits alongside its billion-parameter siblings, and why you might reach for it in

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38:08

Episode 55: From Frittatas to Production LLMs:...

Vanishing Gradients ·

2025/08/12

Traditional software expects 100% passing tests. In LLM-powered systems, that’s not just unrealistic — it’s a feature, not a bug. Eric Ma leads research data science in Moderna’s data science and AI group, and over breakfast at SciPy we explored why AI products break the old rules, what skills different personas bring (and miss), and how to keep systems alive after the launch hype fades. You’ll hear the clink of coffee cups, the murmur of SciPy in the background, and the occasional bite of

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41:17

Episode 54: Scaling AI: From Colab to Clusters — A...

Vanishing Gradients ·

2025/07/18

Colab is cozy. But production won’t fit on a single GPU. Zach Mueller leads Accelerate at Hugging Face and spends his days helping people go from solo scripts to scalable systems. In this episode, he joins me to demystify distributed training and inference — not just for research labs, but for any ML engineer trying to ship real software. We talk through: • From Colab to clusters: why scaling isn’t just about training massive models, but serving agents, handling load, and speeding up iteration

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44:49

Episode 53: Human-Seeded Evals & Self-Tuning Agents:...

Vanishing Gradients ·

2025/07/08

Demos are easy; durability is hard. Samuel Colvin has spent a decade building guardrails in Python (first with Pydantic, now with Logfire), and he’s convinced most LLM failures have nothing to do with the model itself. They appear where the data is fuzzy, the prompts drift, or no one bothered to measure real-world behavior. Samuel joins me to show how a sprinkle of engineering discipline keeps those failures from ever reaching users. We talk through: • Tiny labels, big leverage: how five

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28:38

Episode 52: Why Most LLM Products Break at Retrieval...

Vanishing Gradients ·

2025/07/02

Most LLM-powered features do not break at the model. They break at the context. So how do you retrieve the right information to get useful results, even under vague or messy user queries? In this episode, we hear from Eric Ma, who leads data science research in the Data Science and AI group at Moderna. He shares what it takes to move beyond toy demos and ship LLM features that actually help people do their jobs. We cover: • How to align retrieval with user intent and why cosine similarity is not the

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47:41

Episode 51: Why We Built an MCP Server and What Broke...

Vanishing Gradients ·

2025/06/26

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

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27:42

Episode 50: A Field Guide to Rapidly Improving AI...

Vanishing Gradients ·

2025/06/17

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: 🔍 Why most teams

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1:21:45

Episode 49: Why Data and AI Still Break at Scale (and...

Vanishing Gradients ·

2025/06/05

If we want AI systems that actually work in production, we need better infrastructure—not just better models. In this episode, Hugo talks with Akshay Agrawal (Marimo, ex-Google Brain, Netflix, Stanford) about why data and AI pipelines still break down at scale, and how we can fix the fundamentals: reproducibility, composability, and reliable execution. They discuss: 🔁 Why reactive execution matters—and how current tools fall short 🛠️ The design goals behind Marimo, a new kind of Python notebook ⚙️

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1:04:25

Episode 48: HOW TO BENCHMARK AGI WITH GREG KAMRADT

Vanishing Gradients ·

2025/05/23

If we want to make progress toward AGI, we need a clear definition of intelligence—and a way to measure it. In this episode, Hugo talks with Greg Kamradt, President of the ARC Prize Foundation, about ARC-AGI: a benchmark built on Francois Chollet’s definition of intelligence as “the efficiency at which you learn new things.” Unlike most evals that focus on memorization or task completion, ARC is designed to measure generalization—and expose where today’s top models fall short. They discuss: 🧠 Why

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1:19:12

Episode 47: The Great Pacific Garbage Patch of Code...

Vanishing Gradients ·

2025/04/07

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

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1:08:57

Episode 46: Software Composition Is the New Vibe Coding

Vanishing Gradients ·

2025/04/03

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

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1:17:30

Episode 45: Your AI application is broken. Here’s...

Vanishing Gradients ·

2025/02/20

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

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1:34:11

Episode 44: The Future of AI Coding Assistants: Who’s...

Vanishing Gradients ·

2025/02/04

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

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1:01:03

Episode 43: Tales from 400+ LLM Deployments: Building...

Vanishing Gradients ·

2025/01/16

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

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62 results

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