## 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.