MLOps : DevOps Applied to Machine Learning Projects

In the world of traditional software development, a set of practices known as DevOps has made it possible to get software into production within minutes and keep it running reliably.

DevOps relies on tools, automation and workflows to eliminate complexity and allow developers to focus on the real problems at hand. This approach has been so successful that many companies have already mastered it.

DevOps allows for a reduction in time to market and an improvement in the quality of software products thanks to 3 principles

• Discipline: development and operations teams must work together, towards a common goal, sharing all information.
• Automation: everything that can be automated must be automated: build, test, deployment. This has the dual objective of reducing the time required for deployment and the number of non-quality issues, as manual steps are prone to human error.
• Monitoring: collect and monitor important metrics, both on the business side (number of users connected, number of orders, etc.) and on the operations side (percentage of CPU / RAM use of servers, number of errors, etc.)

Following the massive adoption of DevOps and the significant increase in the use of Machine Learning and AI in the enterprise, DevOps concepts have been taken up in data projects. While the objectives and principles remain the same overall, certain specificities of Machine Learning have necessitated the creation of a…