MLOps
Equal ExpertsContact UsPlaybooks
  • 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
Powered by GitBook
On this page
Export as PDF
  1. Principles

Apply continuous delivery

Machine learning solutions are complex software and should use best practice

We want to be able to amend how our machine learning models consume data and integrate with other business systems in an agile fashion as the data environment, downstream IT services and needs of the business change. Just like any piece of working software, continuous delivery practices should be adopted in machine learning to enable regular updates of those integrations in production. Teams should adopt typical continuous delivery techniques, use Continuous Integration and Deployment (CI/CD) approaches; utilise Infrastructure as Code (Terraform, ansible, packer, etc.) and work in small batches to have fast and reasonable feedback, which is key to keeping a continuous improvement mindset.

PreviousA machine learning service is a productNextEvaluate and monitor algorithms throughout their lifecycle

Last updated 3 years ago