Other articles

  1. Training and Deploying an ML Model

    Thu 15 July 2021

    This post is a collection of several different techniques that I wanted to learn. In this blog post I'll be using open source python packages to do automated data exploration, automated feature engineering, automated machine learning, and model validation. I'll also be using docker and kubernetes to deploy the model. I'll cover the entire codebase of the model, from the initial data exploration to the deployment of the model behind a RESTful API in Kubernetes.

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  2. A RESTful ML Model Service

    Thu 29 April 2021

    Sometimes you find yourself writing the same code over and over. When that starts happening you know it's time to take what you've learned and create a reusable piece of code that can be applied in the future. Because of the experience that we've gained in writing previous blog posts, I think that it is a good time to make a reusable service that can host any number of machine learning models.

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  3. Introducing the ml_base Package

    Mon 22 February 2021

    The ml_base package defines a common set of base classes that are useful for working with machine learning model prediction code. The base classes define a set of interfaces that help to write ML code that is reusable and testable. The core of the ml_base package is the MLModel class which defines a simple interface for doing machine learning model prediction. I this blog post, we'll show how to use the MLModel class.

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  4. 10 Ways to Deploy a Machine Learning Model

    Wed 28 October 2020

    In previous blog posts we've seen how it is possible to deploy the same model in ten different ways. The model itself was developed one time and released as a package, which was then used in each deployment. These blog posts started as an exercise in finding new and interesting ways to deploy an ML model, so we decided to write this blog post about some of the things that we've learned along the way. In order to be able to deploy the same model in 10 different ways, we needed to build the model so that it was not incompatible with all the different ways we wanted to deploy it. We also needed to make it easy to install and to make sure that the model published metadata about itself. All of these features of the model became very important once we needed to deploy it into a real software system.

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