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

Overview

Machine learning, and by extension most artificial intelligence, has grown from a niche idea to a tool that is being applied in multiple areas and industries.

At EE we have been involved in developing and deploying machine learning for a number of applications, including to:

  • Assess cyber-risk

  • Evaluate financial risk

  • Improve search and recommendations in retail web sites

  • Price used vehicles

  • Improve logistics and supply chains

An ML solution depends on both the algorithm - which is code - and the data used to develop and train that algorithm. For this reason, developing and operating solutions that use ML components is different to standard software development.

This playbook brings together our experiences working with algorithm developers to make machine learning a normal part of operations. It won’t cover the algorithm development itself - that is the work of the data scientists. Instead it covers what you need to consider when providing the architecture, tools and infrastructure to support their work and integrate their outputs into the business.

It is a common mistake to focus on algorithms - after all they are very clever, require deep expertise and insight and in some cases seem to perform miracles. But in our experience, obtaining business value from algorithms requires engineering to support the algorithm development part of the process alongside integrating the machine learning solution into your daily operations. To unlock this value you need to:

  • Collect the data that drives machine learning and make it available to the data scientists who develop machine learning algorithms

  • Integrate these algorithms into your everyday business

  • Configuration control, deploy and monitor the deployed algorithms

  • Create fast feedback loops to algorithm developers

As with all of our playbooks we have written this guide in the spirit of providing helpful advice to fellow developers creating ML solutions. If you are starting on the ML journey we hope you are not daunted by all the things covered in this playbook. Starting small and being lean in your implementation choices at the start is perfectly fine and will probably help you to iterate quicker.

NextKey terms

Last updated 11 months ago

Download a version of the MLOps playbook

PDF