This is the second part in a series of articles demonstrating best practices for engineering ML pipelines and deploying them to production. In the first part we focused on project setup - everything from codebase structure to configuring a CI/CD pipeline and making an initial deployment of a skeleton pipeline …

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The is the first in a series of articles demonstrating how to engineer a machine learning pipeline and deploy it to a production environment. We’re going to assume that a solution to a ML problem already exists within a Jupyter notebook, and that our task is to engineer this …

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• 17th August 2019 - updated to reflect changes in the Kubernetes API and Seldon Core.
• 14th December 2020 - the work in this post forms the basis of the Bodywork MLOps tool - read about it here.

A common pattern for deploying Machine Learning (ML) models into production environments - e.g. ML models …

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Conducting a Bayesian data analysis - e.g. estimating a Bayesian linear regression model - will usually require some form of Probabilistic Programming Language (PPL), unless analytical approaches (e.g. based on conjugate prior models), are appropriate for the task at hand. More often than not, PPLs implement Markov Chain Monte Carlo …

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Building machine learning and statistical models often requires pre- and post-transformation of the input and/or response variables, prior to training (or fitting) the models. For example, a model may require training on the logarithm of the response and input variables. As a consequence, fitting and then generating predictions from …

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