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

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

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 [https://youtube.com/live/-8Gr9fVVX9g?feature=share] * The LLMOps database [https://www.zenml.io/llmops-database] * All blog posts about the database [https://www.zenml.io/category/llmops] * Anthropic's Building effective agents essay [https://www.anthropic.com/research/building-effective-agents] * Alex on LinkedIn [https://www.linkedin.com/in/strickvl/] * Hugo on twitter [https://x.com/hugobowne] * Vanishing Gradients on twitter [https://x.com/vanishingdata] * Vanishing Gradients on YouTube [https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA] * Vanishing Gradients on Twitter [https://x.com/vanishingdata] * Vanishing Gradients on Lu.ma [https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk]

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

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 [https://www.youtube.com/watch?v=BRIYytbqtP0] * The original podcast episode [https://learnbayesstats.com/episode/122-learning-and-teaching-in-the-age-of-ai-hugo-bowne-anderson] * Alex Andorra on LinkedIn [https://www.linkedin.com/in/alex-andorra/] * Hugo on LinkedIn [https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/] * Hugo on twitter [https://x.com/hugobowne] * Vanishing Gradients on twitter [https://x.com/vanishingdata] * Hugo's "Building LLM Applications for Data Scientists and Software Engineers" course [https://maven.com/s/course/d56067f338]

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

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) [https://www.linkedin.com/in/jepsontaylor/] – CEO and Founder at VEOX Inc., with experience in AI exploration, genetic programming, and deep learning. * Joe Reis [https://www.linkedin.com/in/josephreis/] – Co-founder of Ternary Data and author of Fundamentals of Data Engineering. * Juan Sequeda [https://www.linkedin.com/in/juansequeda/] – 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 [https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?utm_campaign=8123d0&utm_medium=partner&utm_source=instructor]. 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 [https://x.com/hugobowne] * Hugo on LinkedIn [https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/] * Vanishing Gradients on twitter [https://x.com/vanishingdata] * "Building LLM Applications for Data Scientists and Software Engineers" course [https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?utm_campaign=8123d0&utm_medium=partner&utm_source=instructor].

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

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 [https://maven.com/s/course/d56067f338]. 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 [https://www.youtube.com/live/INryb8Hjk3c?si=0cbb0-Nxem1P987d] * The GPU Glossary [https://modal.com/gpu-glossary] by the Modal team * What We’ve Learned From A Year of Building with LLMs [https://applied-llms.org/] by Charles and friends * Charles on twitter [https://x.com/charles_irl] * Hugo on twitter [https://x.com/hugobowne] * Vanishing Gradients on twitter [https://x.com/vanishingdata]

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

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 [https://www.youtube.com/live/ffS6NWqoo_k] * Google Labs [https://labs.google/] * Ravin's GenAI Handbook [https://ravinkumar.com/GenAiGuidebook/book_intro.html] * Breadboard: A library for prototyping generative AI applications [https://breadboard-ai.github.io/breadboard/] 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 [https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?promoCode=VG25] or by applying the code VG25 at checkout.