Deploying LLMs in Production: Lessons Learned
Hamel Husain is a machine learning engineer who loves building machine learning infrastructure and tools 👷🏼♂️. He leads or 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. Parlance Labs is working with tech-forward companies like Honeycomb and Rechat to accelerate AI-powered features in their products. Hamel has a wealth of experience with end-to-end commercial deployments of LLMs, along with hard-won perspectives from working with these technologies in the wild.
In this live-streamed recording of Vanishing Gradients, Hamel joins your host Hugo Bowne-Anderson, to talk about generative AI, large language models, the business value they can generate, and how to get started.
We’ll delve into
Where Hamel is seeing the most business interest in LLMs (spoiler: the answer isn’t 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.
And much more, all the while grounding our conversation in real-world examples from data science, machine learning, business, and life.
We’ll also show you how to get started with all these tools by fine-tuning the open source Llama2 model live!
