I’m a data scientist specializing in Machine Learning. The purpose of this page is to serve as a central point of reference for talks and blogs I write.
Machine Learning Ops, A collection of resources on how to facilitate Machine Learning Ops with GitHub.
fastpages, an easy to use blogging platform for Jupyter Notebooks.
CodeSearchNet, Datasets, tools, and benchmarks for representation learning of code.
Issue Label Bot, A GitHub App powered by machine learning.
Links to associated github repos (and live demos if available) are located in blog articles.
Introducing fastpages, An easy to use blogging platform with extra features for Jupyter Notebooks. by Jeremy Howard & Hamel Husain, GitHub Repo
GitHub Actions: Providing Data Scientists With New Superpowers by Jeremy Howard & Hamel Husain.
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search: by Miltiadis Allamanis, Marc Brockschmidt, Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit GitHub Repo
How To Create Natural Language Semantic Search for Arbitrary Objects With Deep Learning. (Related: GitHub engineering blog article, Live demo)
Gradient Descent by Weights & Biases: A discussion on Automated Machine Learning, CodeSearchNet, GitHub Actions and MLOps: Video
GitHub Universe 2019: “Machine Learning Ops With GitHub Actions & Kubernetes”. Video
Data Skeptic Interview, Jan 2018: “Semantic Search at Github”.