1. Decorator Pattern for ML Models

    Sun 27 February 2022

    The decorator pattern is a software engineering pattern that allows software to be more flexible, more reusable, and more cohesive. In this blog post, we’ll explore how decorators work, how to implement them, and how to apply them to the MLModel base class.

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  2. Property-Based Testing for ML Models

    Fri 03 September 2021

    Property-based testing is a form of software testing that allows developers to write more comprehensive tests for software components. Property-based tests work by asserting that certain properties of the software component under test hold over a wide range of inputs. Property-based tests rely on the generation of inputs for a component and are a form of generative testing. When doing property-based testing it is useful to think in terms of invariants within the software component that we are testing. An invariant is a condition or assumption that we expect will never be violated by the component.

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  3. 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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  4. 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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  5. 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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