The Role of CI/CD in MLOps

If you are working in a big tech company on ML projects, chances are you are working on some version of Continuous Integration / Continuous Deployment (CI/CD). It represents a high level of maturity in MLOps with Continuous Training (CT) at the top. This level of automation really helps ML engineers to solely focus on experimenting with new ideas while delegating repetitive tasks to engineering pipelines and minimizing human errors. On a side note, when I was working at Meta, the level of automation was of the highest degree. That was simultaneously fascinating and quite frustrating! I had spent so many years learning how to deal with ML deployment and management that I had learned to like it. I was becoming good at it, and suddenly all that work seemed meaningless as it was abstracted away in some automation. I think this is what many people are feeling when it comes to AutoML: a simple call to a "fit" function seems to replace what took years of work and experience for some people to learn. There are many ways to implement CI/CD/CT for Machine Learning but here is a typical process: - The experimental phase - The ML Engineer wants to test a new idea (let's say a new feature transformation). He modifies the code base to implement the new transformation, trains a model, and validates that the new transformation indeed yields higher performance. The resulting outcome at this point is just a piece of code that needs to be included in the master repo. - Continuous integration - The engineer then creates a Pull Request (PR) that automatically triggers unit testing (like a typical CI process) but also triggers the instantiation of the automated training pipeline to retrain the model, potentially test it through integration tests or test cases and push it to a model registry. There is a manual process for another engineer to validate the PR and performance reading of the new model.  - Continuous deployment - Activating a deployment triggers a canary deployment to make sure the model fits in a serving pipeline and runs an A/B test experiment to test it against the production model. After satisfactory results, we can propose the new model as a replacement for the production one. - Continuous training - as soon as the model enters the model registry, it deteriorates and you might want to activate recurring training right away. For example, each day the model can be further fine-tuned with the new training data of the day, deployed, and the serving pipeline is rerouted to the updated model. The Google Cloud documentation is a good read on the subject: https://lnkd.in/gA4bR77x  https://lnkd.in/g6BjrBvS ---- Receive 50 ML lessons (100 pages) when subscribing to our newsletter: TheAiEdge.io #machinelearning #datascience #artificialintelligence

CI/CD Pipeline for Machine Learning: A Comprehensive Guide I've created a visual breakdown of a modern ML CI/CD pipeline, demonstrating the three critical stages of ML model deployment: Step 1: Unit Tests - Feature Retrieval → Validation → Training → Evaluation → Validation → Handover - Each component undergoes rigorous unit testing to ensure individual functionality Step 2: Integration Tests - Introduces Feature Store and Model Registry - Tests interactions between components - Validates data flow and model transitions - Ensures seamless integration of the entire pipeline Step 3: Delivery - Production-ready pipeline with monitoring - Feature Store for consistent data management - ML Metadata Store for model tracking - Model Registry for version control - Orchestration and monitoring systems for reliability Key Benefits: • Ensures model reproducibility • Maintains quality through automated testing • Streamlines deployment process • Enables continuous monitoring and updates This pipeline architecture helps bridge the gap between ML development and production deployment, ensuring reliable and scalable ML systems.

 CI/CD for Machine Learning: Bridging the Gap Between Models & Production Machine Learning isn't just about building models—it’s about making them robust, reliable, and ready for production! That’s where CI/CD for ML comes in. Just like software development, continuous integration, testing, and deployment are crucial to ensure models perform optimally in real-world scenarios . 🔹 Step 1: Unit Tests Before anything goes live, we test each component of the ML pipeline—feature retrieval, validation, training, evaluation, and model validation—to ensure correctness. 🔹 Step 2: Integration Tests The entire ML pipeline is tested in a pre-prod environment, ensuring seamless integration across all stages. Models are stored in a registry, ready for the next step. 🔹 Step 3: Delivery & Monitoring With the orchestrator and monitoring systems in place, we automate deployments across pre-prod and production environments—ensuring stability, tracking performance, and retraining when needed. Key Takeaway: A well-defined CI/CD pipeline for ML reduces errors, accelerates deployment, and ensures your models stay reliable in production! What challenges have you faced in setting up ML pipelines?