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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PreviousNot taking into consideration the downstream application of the model

Last updated 3 years ago

We’d like to thank everyone within Equal Experts who has shared their wisdom and experiences with us, and have made this playbook possible.

Main authors

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Thanks also to

Khalil Chourou, Adam Fletcher, Matteo Guzzo, Uttam Kini, Oshan Modi, Shaun McGee, Austin Poulton, Katharina Rasch, Jake Saunders, Isabell Britsch

Paul Brabban
Jon Carney
Simon Case
Scott Cutts
Claudio Diniz
Bas Geerdink
Thorben Louw
Jennifer Stark
Rajesh Thiagarajan