The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Exploring the FastAI Tooling Ecosystem with Hamel Husain - #532
Nov 1, 2021
Hamel Husain, a Staff Machine Learning Engineer at GitHub, has contributed to pivotal open-source projects like FastAI and nbdev. He shares insights from his journey through Silicon Valley and discusses the ML tooling gaps he encountered. The conversation highlights innovative tools like MBDev that integrate coding and documentation, while also exploring how FastAI enhances data science workflows. Hamel expresses excitement for future ML tools and the seamless automation capabilities of GitHub Actions, promising a more efficient coding experience.
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The Need for Better ML Tooling
- Hamel's experience across various data science roles revealed a consistent lag in ML tooling and infrastructure.
- This realization led him to focus on building and improving these crucial components.
Manual Model Deployment at Airbnb
- At Airbnb, Hamel reviewed an LTV model with a surprisingly manual deployment process.
- The model involved copying coefficients from an R script into Excel, then converting them into a SQL query for Airflow.
From Semantic Search to nbdev
- Hamel's open-source work at GitHub, including semantic search with FastAI, led to his deeper involvement with the project.
- This involvement grew organically through contributions to documentation, CI/CD, and eventually, core tools like nbdev.