About
About Will High
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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Will High
Machine learning scientist
Publications
Will High’s Publications
Posts
Machine Learning Likelihood, Loss, Gradient, and Hessian Cheat Sheet
Cheat sheet for likelihoods, loss functions, gradients, and Hessians.
Deploy Custom Shiny Apps to AWS Elastic Beanstalk
How I tricked AWS into serving R Shiny with my local custom applications using rocker and Elastic Beanstalk.
Debugging Metaflow Jobs
The combination of an IDE, a Jupyter notebook, and some best practices can radically shorten the Metaflow development and debugging cycle.
Metaflow Best Practices for Machine Learning
Some of these are specific to Metaflow, some are more general to Python and ML.
Machine Learning Model Selection with Metaflow
Configurable, repeatable, parallel model selection using Metaflow, including randomized hyperparameter tuning, cross-validation, and early stopping.
Where Those Loss Constants Come From
Here’s where that n and that 2 come from in the square-loss objective function, in gory detail.
Parallel Grep and Awk
I get a nearly 6x speedup over standard grep by using GNU parallel.
Hacking a Serverless Machine-Learning Scoring Microservice with AWS Lambda
In this post I’ll attempt to hack a scikit-learn model prediction microservice with AWS Lambda.
Guaranteeing k Samples in Streaming Sampling Without Replacement
If you need $k$ samples out of $N$ in Hive or Pig, typically you’d naively choose $p = k/N$, but this only gives you $k$ on average.
The Streaming Distributed Bootstrap
The streaming distributed bootstrap is a really fun solution, and I’ve mocked up a Python package to test it out.