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Build. Test. Train. Deploy. Monitor. Retrain.
The quality of your models depends on the speed at which you can train and retrain them.
Nondeterministic output makes reproducibility a challenge
Ad hoc changes across your team and dataset make it difficult to trace the impact of a single change.
Inadequate compute can bottleneck your entire project
Building machine learning models at scale requires fast, flexible compute, in the cloud or on your own machines.
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Frequently asked questions
What is CI/CD for machine learning?
CI/CD for machine learning is a set of practices and tools that enable automated testing, training, validation, and deployment of machine learning models and code. It helps ensure a consistent and reliable ML development pipeline.
What is MLOps, and how does it relate to CI/CD for machine learning?
MLOps combines machine learning (ML) development with traditional DevOps principles, including collaboration, automation, frequent testing, and rapid iteration. MLOps extends these practices to encompass the entire ML lifecycle, including model training, validation, deployment, and monitoring.
Why should I use CI/CD and MLOps for machine learning?
CI/CD and MLOps enhance collaboration, accelerate development, and improve model quality by automating processes like testing, training, and deployment. They can drastically reduce manual errors and ensure reproducibility, empowering teams to develop and ship reliable models quickly and confidently.
How does CI/CD for machine learning work?
It integrates ML development with version control, automated testing, and continuous deployment. Developers commit code changes to a repository, and CI/CD pipelines automatically build, train, test, and deploy the ML models.
If a model doesn’t pass all the tests and meet quality standards, the CI/CD pipeline prevents deployment and immediately notifies the responsible teams so they can remediate and refine the model. If performance declines in production due to drift, the pipeline can automatically train and deploy a new version of the model, ensuring your models remain reliable and up to date.
How does CI/CD handle model versioning and data management?
CI/CD pipelines can integrate with tools for model versioning (e.g., DVC) and data management (e.g., DataRobot) to ensure reproducibility and track changes in your ML pipeline.
What are the key benefits of CI/CD for machine learning?
Key benefits include faster development cycles, improved model quality, reduced errors, better collaboration among data scientists and developers, and simplified deployment.
At the highest level, these improvements translate to a better experience for customers, less repetitive manual work for data scientists and engineers, and reduced operational costs for AI/ML organizations.
How do I get started with CI/CD for machine learning?
Start by defining your ML pipeline, selecting the right CI/CD tools, and integrating them into your development process.
You can sign up for a free CircleCI account and follow our starter tutorial to learn the features CircleCI has available to support ML teams. Our expert support team can help expedite your onboarding process and optimize your pipelines as you grow and scale.
How do I get notified about CircleCI’s latest MLOps and LLMOps integrations?
To receive updates on CircleCI’s AI roadmap and early access to new features to accelerate your AI and ML projects, sign up for the waitlist.