Topic: Machine Learning Engineering

Machine learning is useless unless the models are being used. Machine learning engineering is the discipline of operationalizing those models by building scalable APIs and systems around them. My articles about machine learning engineering can be found here:

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  Machine Learning Deployment:
  Return Actions, Not Scores
  A poorly designed machine learning model API will leave you trapped. Properly hiding your implementation will make life much easier!
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  Using Scikit-learn Pipelines with Pandas Dataframes
  Pandas and scikit-learn are two important libraries for building machine learning models. Here is how to get them to work together.
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  Computing Machine Learning Features in Real-time
  Models often derive great value from real-time features, but computing them is hard because it has to be done quickly. Here is one way I have done it successfully.
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  Machine Learning Deployment: Shadow Mode
  Deploying machine learning models is hard; Shadow Mode is one way to make testing a little easier.