[Hands-on] CI/CD in MLOps Workflows

The full MLOps blueprint.

Part 18 of the MLOps and LLMOps crash course is now available, where we understand and learn the entirety of CI/CD workflows in ML systems with all necessary theoretical details and hands-on examples.

MLOps and LLMOps crash course Part 18 →

Traditional CI/CD focuses on delivering code changes quickly and safely. With ML, we need to extend this idea to data and models too.

This means our pipelines must not only run code tests, but also validate input data, retrain models, track model metrics, and then deploy or push those models.

To explain, we cover the entire end-to-end details, covering:

• CI for ML

  • Data CI: validating data

  • Code CI: testing code

  • Model CI: testing models and quality

• CD for ML

  • Model packaging and serving

  • Automated deployments

• Hands-on: CI/CD pipeline with AWS

Just like all our past series on MCP, RAG, and AI Agents, this series is both foundational and implementation-heavy, walking you through everything that a real-world ML system entails:

A conceptual ML system in production, depicting the share of ML model codes in the complete project

• Part 1 covered foundations and MLOps system principles →

• Part 2 covered ML system lifecycle with hands-on project →

• Part 3 covered reproducibility and versioning for ML systems →

• Part 4 also covered reproducibility and versioning for ML systems →

• Part 5 covered data and pipeline engineering →

• Part 6 covered building scalable data pipelines →

• Part 7 covered Spark, and orchestration + workflow management →

• Part 8 covered the modeling phase of the MLOps lifecycle from a system perspective →

• Part 9 covered fine-tuning and model compression/optimization →

• Part 10 expanded on the model compression discussed in Part 9 →

• Part 11 covered the deployment phase of the MLOps lifecycle →

• Part 12 dived into Kubernetes (with implementation) →

• Part 13 covered the cloud infrastructure components like VMs, hypervisors, and virtualization, containers & orchestration, and IAM, along with patterns for ML workloads in Cloud →

• Part 14 covered a full hands-on deep dive on AWS →

• Part 15 continued with the hands-on deep dive on AWS →

• Part 16 covered fundamentals of monitoring and observability, explaining why models degrade, model degradation, and techniques to detect drift, logging, and a hands-on demo →

• Part 17 covered everything you need to know about Monitoring and Observability in production, with tooling, like Evidently AI, Prometheus, Grafana, along with a full hands-on end-to-end ML monitoring demo.

This MLOps and LLMOps crash course provides a thorough explanation and systems-level thinking to build AI models for production settings.

Just like the MCP crash course, each chapter will clearly explain necessary concepts, provide examples, diagrams, and implementations.

Thanks for reading!