CI/CD for Machine Learning is a set of practices and tools designed to automate the testing, training, validation, and deployment of Machine Learning models and code. It helps ensure the consistency and reliability of the machine learning development process.

MLOps combines Machine Learning (ML) development with traditional DevOps principles of collaboration, automation, frequent testing, and rapid stacking.MLOps extends these practices to cover the entire Machine Learning lifecycle, including model training, validation, deployment, and monitoring.

CI/CD and MLOps increase collaboration, accelerate development, improve model quality, significantly reduce manual errors, ensure repeatability, and enable teams to develop and deliver reliable models quickly and confidently by automating processes such as testing, training, and deployment.

It integrates machine learning development with version control, automated testing and continuous deployment. Developers commit code changes to a codebase, and a CI/CD pipeline automatically builds, trains, tests, and deploys machine learning models.
If a model does not pass all tests or does not meet quality standards, the CI/CD pipeline will prevent deployment and immediately notify the appropriate team so they can make repairs and improvements. If performance degrades in the production environment due to data drift, the pipeline can automatically train and deploy a new version of the model to ensure it remains reliable and up-to-date.

CI/CD pipelines can be integrated with model versioning (e.g., DVC) and data management (e.g., DataRobot) tools to ensure repeatability and track changes in the machine learning pipeline.

Key benefits include faster development cycles, higher model quality, fewer bugs, better collaboration between data scientists and developers, and a streamlined deployment process.
At the highest level, these improvements result in a better customer experience, less repetitive manual work for data scientists and engineers, and lower operating costs for AI/ML organizations.

Start by defining your ML pipeline, choosing the right CI/CD tools, and integrating them into your development process.
You can sign up for a free CircleCI account and follow our Getting Started tutorial to learn about the features available to support ML teams with CircleCI. Our expert support team can help you speed up the onboarding process and optimize your pipeline as you grow and expand.