MLOps
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  • Overview
    • Key terms
  • What is MLOps
  • Principles
    • Solid data foundations
    • Provide an environment that allows data scientists to create and test models
    • A machine learning service is a product
    • Apply continuous delivery
    • Evaluate and monitor algorithms throughout their lifecycle
    • MLOps is a team effort
  • Practices
    • Collect performance data
    • Ways of deploying your model
    • How often do you deploy a model?
    • Keep a versioned model repository
    • Measure and proactively evaluate quality of training data
    • Testing through the ML pipeline
    • Business impact is more than just accuracy - understand your baseline
    • Regularly monitor your model in production
    • Monitor data quality
    • Automate the model lifecycle
    • Create a walking skeleton/steel thread
    • Appropriately optimise models for inference
  • Explore
  • Pitfalls (Avoid)
    • User Trust and Engagement
    • Explainability
    • Avoid notebooks in production
    • Poor security practices
    • Don’t treat accuracy as the only or even the best way to evaluate your algorithm
    • Use machine learning judiciously
    • Don’t forget to understand the at-inference usage profile
    • Don’t make it difficult for a data scientists to access data or use the tools they need
    • Not taking into consideration the downstream application of the model
  • Contributors
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  1. Pitfalls (Avoid)

Use machine learning judiciously

Despite the hype, machine learning should not be the default approach to solve a problem. Complex problems, tightly tied to how our brains work like machine vision and natural language processing, are generally accepted as best tackled with artificial intelligence based on machine learning. Many real-world problems affecting a modern organisation are not of this nature and applying machine learning where it is not needed brings ongoing complexity, unpredictability and dependence on skills that are expensive to acquire and maintain. You could build a machine learning model to predict whether a number is even or odd - but you shouldn’t.

We typically recommend trying a non-machine learning based solution first. Perhaps a simple, rules-based system might work well enough to be sufficient. If nothing else, attempting to solve the problem with a non machine-learning approach will give you a baseline of complexity and performance that a machine learning-based alternative can be compared with.

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Last updated 3 years ago