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1 Why You Need to Grow Your Customer’s Decision Confidence with Brent Adamson and Karl Schmidt 36:25
Episode 21: Deploying LLMs in Production: Lessons Learned
Manage episode 383681385 series 3317544
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
53 episodes
Manage episode 383681385 series 3317544
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
53 episodes
All episodes
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1 Episode 53: Human-Seeded Evals & Self-Tuning Agents: Samuel Colvin on Shipping Reliable LLMs 44:49

1 Episode 52: Why Most LLM Products Break at Retrieval (And How to Fix Them) 28:38

1 Episode 51: Why We Built an MCP Server and What Broke First 47:41

1 Episode 50: A Field Guide to Rapidly Improving AI Products -- With Hamel Husain 27:42

1 Episode 49: Why Data and AI Still Break at Scale (and What to Do About It) 1:21:45

1 Episode 48: HOW TO BENCHMARK AGI WITH GREG KAMRADT 1:04:25

1 Episode 47: The Great Pacific Garbage Patch of Code Slop with Joe Reis 1:19:12

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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