Other articles

  1. Data Enrichment for ML Model Deployments

    Sun 01 May 2022

    Machine learning models need data to make predictions. When deploying a model to a production setting, this data is not necessarily available from the client system that is requesting the prediction. When this happens, some other source is needed for the data that is required by the model but not provided by the client system. The process of accessing the data and joining it to the client's prediction request is called data enrichment. In all cases, the model itself should not need to be modified in order to do data enrichment, the process should be transparent to the model. In this blog post, we'll show a method for doing data enrichment that does not require the model itself to be modified.

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  2. 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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  3. 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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  4. 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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