Overview
What are the components of a machine learning system?
Overview - Infrastructure and Tooling

Summary

  • Google's seminal paper "Machine Learning: The High-Interest Credit Card of Technical Debt" states that if we look at the whole machine learning system, the actual modeling code is very small. There are a lot of other code around it that configure the system, extract the data/features, test the model performance, manage processes/resources, and serve/deploy the model.
  • The data component:
    • Data Storage - How to store the data?
    • Data Workflows - How to process the data?
    • Data Labeling - How to label the data?
    • Data Versioning - How to version the data?
  • The development component:
    • Software Engineering - How to choose the proper engineering tools?
    • Frameworks - How to choose the right deep learning frameworks?
    • Distributed Training - How to train the models in a distributed fashion?
    • Resource Management - How to provision and mange distributed GPUs?
    • Experiment Management - How to manage and store model experiments?
    • Hyper-parameter Tuning - How to tune model hyper-parameters?
  • The deployment component
    • Continuous Integration and Testing - How to not break things as models are updated?
    • Web - How to deploy models to web services?
    • Hardware and Mobile - How to deploy models to embedded and mobile systems?
    • Interchange - How to deploy models across systems?
    • Monitoring - How to monitor model predictions?
  • All-In-One: There are solutions that handle all of these components!
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Summary