While continuous integration (CI) and continuous delivery (CD) have been common practice in traditional software development for some time, the machine learning space has only been catching up over the past few years. 

In this piece, we’ll look at some of the challenges with CI/CD in ML, how to overcome them, and the tools that can help you along the way. 

 

What is continuous integration?

Continuous integration is a collaborative practice for software and machine learning (ML) model development. Instead of working in isolation and merging work at the end, CI encourages frequent integration of code changes into a shared repository. Each time a developer completes a piece of code, it is merged with the main codebase. This process is automated, compiling the code and running tests to make sure everything functions correctly. By identifying issues early, CI promotes teamwork, enhances code quality, and prevents major problems down the line.

What is continuous integration in MLOps?

In MLOps, continuous integration involves frequently merging machine learning code changes into a shared version control repository. This practice is followed by an automated build and testing process to ensure compatibility with the existing ML model and codebase. Continuous integration is vital in MLOps as it fosters collaboration, maintains code quality, and supports efficient ML model development.

The steps of continuous integration in MLOps

1. Code commit: The CI pipeline begins with developers sharing their code changes with a version control system, such as Git. This collaborative effort promotes consistency and organization within the codebase.
2. Automated build: Once the code is committed, an automated build process compiles the code, checks for errors or missing dependencies, and generates executable artifacts ready for testing. This process helps all code components integrate seamlessly.
3. Unit tests: Unit tests verify the functionality of individual code components in isolation, ensuring each part works correctly and meets its intended purpose. This step validates the fundamental building blocks of the application.
4. Integration tests: Integration tests examine the interactions between different code components so they function cohesively as a system. This step helps certify that all parts work together harmoniously, contributing to the system’s overall performance.

The benefits of continuous integration in MLOps

1. Early issue detection: Continuous integration enables early identification of issues by running tests immediately after changes are made. This proactive approach simplifies debugging and prevents problems from escalating.
2. Stable model performance: By running automated tests after changes are made, continuous integration means that ML models maintain their performance and reliability. This process protects the integrity of your models against disruptions from new updates and helps you spot data drift and other issues that degrade model performace.
3. Faster iterations: Continuous integration automates the integration, build, and testing processes, allowing data scientists to experiment with new ideas and improvements more rapidly. This automation accelerates development cycles and enhances innovation.

What is continuous delivery?

Continuous delivery in MLOps automates the deployment of machine learning models to production environments, providing seamless updates. The practice simplifies the model delivery process, allowing data scientists to release their model effortlessly. With continuous delivery, the deployment process is managed by automated pipelines that run tests and validations. That means your model is always production-ready.

This enables data scientists and machine learning engineers to focus on innovation and model improvement, knowing that deployment is handled smoothly and reliably.

The difference between continuous delivery and continuous deployment

Continuous delivery and continuous deployment both aim to automate update delivery, leading to frequent, reliable, and efficient releases. They each streamline the workflow by automating the build, testing, and deployment processes, thereby fostering collaboration among teams. However, there are key differences between the two practices.

In continuous delivery for MLOps, machine learning models are always maintained in a deployable state. Automated pipelines validate model changes and prepare them for deployment. However, the actual deployment to production requires human approval or intervention. This practice ensures that models are ready for deployment at any time while providing control over when to deploy.

In contrast, continuous deployment automates the entire process, including the final step of deploying to production. Once code changes pass automated testing and staging, they are automatically released to production without human intervention. This approach leads rapid and continuous updates to end users.

The choice between continuous delivery and continuous deployment depends on the organization’s risk tolerance and confidence in their automated processes. Continuous delivery offers more control over deployment timing, while continuous deployment emphasizes speed and automation, making new features available immediately.

Steps in continuous delivery in machine learning

Continuous delivery builds upon the initial steps of continuous integration, sharing the first four steps: Code Commit, Automated Build, Unit Tests, and Integration Tests. The additional steps specific to continuous delivery are as follows:

1. Deployment to staging: After successful automated tests in CI, the machine learning model is deployed to a staging environment resembling the production environment. This stage allows for further validation and testing in a controlled setting.
2. User acceptance testing (UAT): A select group of users tests the model to make certain it meets their requirements and expectations. Feedback is collected for necessary adjustments.
3. Automated deployment to production: If the model passes all previous stages, it is automatically deployed to the production environment. This process is managed by continuous delivery tools and follows predefined workflows.
4. Monitoring and feedback: Continuous monitoring of the ML model in production helps identify any issues or performance deviations. This feedback loop leads to prompt responses to potential problems.

Benefits of continuous delivery

1. Rapid and reliable deployments: Continuous delivery automates the deployment process so that software updates reach users swiftly and consistently. This saves time and effort, allowing for the timely delivery of new features and improvements.
2. Stable model performance: Automated testing means that ML models remain dependable even after code changes. This maintains the accuracy and reliability of models, providing users with consistent results.
3. Collaboration and visibility: Continuous delivery fosters collaboration among data scientists and ML engineers. It provides a shared code repository and immediate feedback on changes, promoting teamwork and knowledge exchange.
4. Reduced deployment risks: The automated deployment process minimizes the risk of human errors and inconsistencies during production releases, facilitating secure and reliable deployments, providing peace of mind.

Conclusion

Continuous integration (CI) and continuous delivery (CD) in MLOps is a best practice for a reason. It reduces friction, eliminates rote, manual handoffs, and helps keep models performant and reliable.

CI/CD emphasizes collaborative development, frequent code integration, and early issue detection through automated testing as well as automating the model release process. This automation leads to smooth and reliable deployments to production environments, reducing the need for manual interventions and minimizing deployment risks.

And while CI/CD in MLOps is still less mature than in traditional software DevOps, the gap is narrowing rapidly.  As models get more powerful and automated testing and deployment technologies continue to advance, CI/CD will become even more vital, allowing engineers to build with increased confidence and velocity and organizations to trust that their production models don’t drift and become less performant.