Continuous integration and continuous delivery/deployment (CI/CD) practices originated in software development as a way to streamline the process of building, testing, and deploying applications. As machine learning has become an increasingly critical capability for businesses, CI/CD principles have been adapted to support more automated, scalable ML model development workflows. In this post, we‘ll explore what CI/CD for machine learning entails and why it offers significant benefits for organizations pursuing AI.
What is CI/CD?
CI/CD refers to a set of practices that automate stages of the development process, helping teams rapidly and reliably build, test, and deploy their code changes.
Continuous integration (CI) involves regularly merging developer code changes into a shared repository where builds and tests run automatically. This catches issues early to prevent cascading failures down the line.
Specifically, CI enables multiple developers to seamlessly collaborate on a codebase with:
- Automated building of the full codebase with each change
- Running automated tests (unit, integration) with each build
- Quickly catching any errors or integration issues so they can be fixed immediately
This tight feedback loop results in more rapid iterations and reduced bugs in production.
Continuous delivery/deployment (CD) picks up where CI ends, automating the release process all the way to production deployment.
CD pipelines take the newly integrated code and:
- Automate packaging it for deployment
- Run final tests (smoke, sanity) to validate production-readiness
- Deploy directly to production or staging environments
- Facilitate rapid releases to users
Together, CI and CD enable a fast, automated code to production pipeline.
Overview of a CI/CD pipeline (Image credit: AIMultiple)
CI/CD enables a consistent, automated development process, reducing manual errors and delays. Studies show teams practicing CI/CD can deploy 200 times more frequently than those relying on manual processes [1].
Adapting CI/CD for Machine Learning Projects
The core principles of CI/CD apply directly to machine learning projects, but the pipelines require adaptation to incorporate data and model workflows. Key considerations include:
- Code integration – Models, data processing, and application code integrated, built, and tested in a single pipeline
- Data validation – New data checked for issues before models are trained
- Model training and validation – Models continuously trained on new data and validated before deployment
- Model deployment – Seamless model deployment after automated checks and tests
- Model monitoring – Post-deployment monitoring to check performance and drift
An additional consideration is continuous training (CT) to automatically retrain models on new data. This helps maintain accuracy over time.
CI/CD pipeline tailored for machine learning projects (Image credit: AIMultiple)
So in machine learning, CI/CD pipelines incorporate code, data, and model workflows to fully automate the end-to-end model development lifecycle.
A survey from Algorithmia found that only 25% of data science teams are utilizing CI/CD practices as part of their model development process [2]. But implementing CI/CD for ML is critical for scaling AI initiatives.
Benefits of CI/CD for Machine Learning
Applying CI/CD principles to ML unlocks a number of benefits:
- Faster deployment – New models and updates shipped faster through automated pipelines. A case study found CI/CD reduced model release time from 4 days to just 1 hour [3].
- Enables AI at scale – Consistent processes to efficiently manage many models. CI/CD pipelines have enabled large enterprises to deploy over 100,000 ML models [4].
- Increases accuracy – Automated validation prevents bugs from reaching production. Tests catch many errors missed by manual processes [5].
- Prevents model drift – Continuous training adapts models to new data over time. One retailer saw a 10% lift in model accuracy from continuous training [6].
For organizations scaling their AI capabilities, CI/CD is critical for accelerating and de-risking model development. Studies show 87% of data science projects fail without MLOps practices like CI/CD.
Key benefits of implementing CI/CD for machine learning projects. (Image credit: AIMultiple)
Challenges with CI/CD for Machine Learning
While impactful, adapting CI/CD for machine learning comes with challenges:
- Complex pipelines – Many interdependent components and parameters to orchestrate. It‘s critical to break down workflows into modular, testable segments.
- Computational resources – Training and deployment require elastic compute resources. Cloud infrastructure helps provide flexible scaling.
- Data privacy and security – Securely managing sensitive data across tools. Proper access controls, encryption, and auditing procedures must be implemented.
Successfully implementing CI/CD requires thoughtful design around an organization‘s infrastructure, tools, workflows, and data needs.
Best Practices for CI/CD in ML
Follow these best practices when architecting CI/CD pipelines for machine learning:
- Modular code – Break models and data workflows into reusable components. This enables easier testing and alterations.
- Automated testing – Unit test components and integration test pipelines. Testing early and often reduces downstream issues.
- Version control – Enable cohesive team collaboration around code and models. Track model lineage and revert to previous versions.
- Cloud infrastructure – Leverage scalable, flexible compute resources. Auto-scale resources to meet demands of training or deployment.
- Monitoring and alerts – Get notifications for model or data drift post-deployment. Quickly detect and address changes in production.
Well-designed pipelines enable rapid iteration without compromising accuracy or infrastructure stability.
CI/CD Vendors for Machine Learning
Many vendors offer tools to facilitate building CI/CD pipelines tailored for machine learning:
- Kubeflow Pipelines – Open source toolkit for ML pipeline orchestration on Kubernetes
- Amazon SageMaker Pipelines – Managed service to build, automate, and manage ML workflows
- IBM Cloud Continuous Delivery service – Automates testing, release, and deployment
- Comet.ml – Tracks experiments, models, and metrics across pipeline runs
The right tools provide scaffolding and best practices to implement governable, scalable CI/CD.
Conclusion
CI/CD principles adapted for machine learning projects offer tremendous benefits for model development and deployment. Automating pipelines accelerates experiments while ensuring quality, enabling organizations to deliver impactful AI applications reliably and at scale.
There are challenges to address, but following best practices around modular design, extensive testing, version control, elastic infrastructure, and monitoring helps set teams up for AI success through CI/CD.
References
[1] State of DevOps Report, Puppet 2021 [2] AI Adoption in the Enterprise, Algorithmia 2021 [3] CI/CD for Machine Learning Case Study, Logical Clocks [4] Operationalizing Machine Learning, McKinsey 2018 [5] Advantages of Continuous Integration for ML, Toptal [6] Retailer Sees Lift from Continuous Training, VentureBeat 2019