Hamel Husain

In this discussion with Hamel Husain, founder of Parlance Labs, they dive into the practicalities of leveraging large language models (LLMs) for real-world applications. Husain shares insights on fine-tuning techniques, including tools like Axolotl and the advantages of LoRa for efficient model adjustments. He emphasizes the importance of thoughtful user interface design and systematic evaluation strategies to enhance AI's effectiveness. The conversation also highlights challenges in data curation and the need for accurate metrics in domain-specific projects, ensuring robust AI development.

Join Hamel Husain and Shreya Shankar, AI experts who’ve shaped the best AI Evals cohort, as they dive into the essentials of AI evaluations for Product Managers. They unveil why these evaluations are crucial for successful AI product development, highlighting common pitfalls to avoid. Discover the concept of 'hill climbing' for enhancing AI performance, and learn how improper reliance on subjective measures can lead to pitfalls like the hallucination problem in AI. Their insights provide a valuable blueprint for mastering AI evaluations.

Experts from Amazon, Hex, Modal, Parlance Labs, and UC Berkeley share lessons learned from working with Large Language Models. They discuss the importance of evaluation and monitoring in LLM applications, data literacy in AI, the fine-tuning dilemma, real-world insights, and the evolving roles of data scientists and AI engineers.

Guest Hamel Husain, a machine learning engineer, discusses the business value of large language models (LLMs) and generative AI. They cover common misconceptions, necessary skills, and techniques for working with LLMs. The podcast explores the challenges of working with ML software and chat GPT, the importance of data cleaning and analysis, and deploying LLMs in production with guardrails. They also discuss an AI-powered real estate CRM and optimizing marketing strategies through data analysis.

Hamel Husain, an independent AI consultant with a rich history at Airbnb and GitHub, dives into the mindset shift required for successful AI engineering. He critiques the reliance on vanity metrics, arguing they lead to misconceptions about AI performance. Instead, he champions custom evaluations and error analysis as the backbone of robust AI products. The discussion also highlights the importance of domain expertise in refining AI metrics and encourages an experimentation mindset to foster continuous improvement and reliability in AI systems.