Episode 21: Deploying LLMs in Production: Lessons Learned

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1+ y ago 1:08:11

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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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