Deploying Python ML Models with Bodywork

Posted on Tue 01 December 2020 in machine-learning-engineering • Tagged with python, machine-learning, mlops, kubernetes, bodywork

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Solutions to Machine Learning (ML) tasks are often developed within Jupyter notebooks. Once a candidate solution is found, you are then faced with an altogether different problem - how to engineer the solution into your product and how to maintain the performance of the solution as new instances of data are …


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Deploying Python ML Models with Flask, Docker and Kubernetes

Posted on Thu 10 January 2019 in machine-learning-engineering • Tagged with python, machine-learning, machine-learning-operations, kubernetes

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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 framework - read about it here.

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


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Bayesian Regression in PYMC3 using MCMC & Variational Inference

Posted on Wed 07 November 2018 in data-science • Tagged with machine-learning, probabilistic-programming, python, pymc3

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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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Machine Learning Pipelines for R

Posted on Mon 08 May 2017 in r • Tagged with machine-learning, data-processing

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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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