Episode 21: Deploying LLMs in Production: Lessons Learned

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