CI/CD for Machine Learning: What it is & Benefits in 2024

Artificial intelligence (AI) and machine learning (ML) applications are changing every industry with hundreds of use cases. As the business adoption of AI increases, companies are facing challenges in developing and deploying AI/ML models:

• %87 of the data science projects fail to pass through the production process.
• More than 40% of data scientists say their results are not used by business decision-makers.

As McKinsey’s research on AI adoption shows, the companies that get the most out of AI follow advanced practices such as MLOps in their AI/ML projects. MLOps is a set of practices to automate and streamline the machine learning development process within an organization. In this article, we’ll explore CI/CD pipelines, one of the core components of MLOps.

What are Continuous Integration (CI) and Continuous Delivery/Deployment (CD)?

Continuous integration (CI) and continuous delivery/deployment are DevOps software development practices that are used to deliver software applications to customers by automating the stages of the lifecycle of an application. These methods help software engineer teams quickly create, test, validate, integrate, deliver, and deploy their codes during application development.

CI/CD pipelines are also used in machine learning development as part of MLOps practices to automate the lifecycle of ML models. Here,

• CI refers to automatically testing and validating code as well as data and the model components of an ML project and merging them into a single version. This ensures that the changes made by different data scientists working collaboratively on an ML project integrate well and do not break the model in production.
• CD refers to both continuous delivery and continuous deployment. In continuous delivery, changes to a project component are automatically uploaded or delivered to a central repository after they have been tested and validated. With continuous deployment, these changes in the central repository are automatically published to be accessed by the end user.

How is CI/CD different in software development and ML?

The fundamental purpose of a CI/CD pipeline is not different for software development and ML model development. In both cases, CI/CD aims to automate and streamline building, testing, validating, packaging, and deploying applications.

However, in contrast to software development, ML projects involve developing ML models and training them on datasets, as well as writing code. So, in addition to testing and validating code, CI/CD in machine learning also involves:

• Data validation: Checking the integrity and accuracy of data before it is used by the model in production.
• Model validation: Checking that the model in production performs as expected.

Moreover, since changes occur faster in the context of machine learning compared to software development, MLOps adds another practice to the pipeline: continuous training (CT). CT involves automatically retraining the model in production to ensure that the model continuously adapts to changes in the data.

What are the benefits of CI/CD for machine learning?

• Enables faster deployment: Automated CI/CD pipelines enable companies to build, test, and deploy the changes in code, data, or model much faster compared to manually integrating data or code changes from different team members.
• Enables AI/ML at scale: As companies scale their ML applications, it gets challenging to manage the changes in different models. CI/CD pipelines help companies reliably adopt AI at scale by streamlining the model development and deployment processes.
• Increases accuracy: By automatically testing and validating that the changes integrate well into the model in production, CI/CD practices reduce human errors and prevent downtimes.

Prevents model drifts: CI/CD practices involve automatically retraining the model with incoming data, which helps prevent model drifts by keeping the models in production up-to-date

If you have other questions about CI/CD in machine learning or MLOps, we can help: