Model Deployment in MLOps Lifecycle

...covered from a system design perspective.

Part 11 of the MLOps and LLMOps crash course is now available, where we dive deep into the deployment phase of the MLOps lifecycle, discussing it from a systems perspective.

Read here: MLOps and LLMOps crash course Part 11 →

Modern machine learning systems don’t deliver value until their models are reliably deployed and monitored in production.

Hence, in this and the next few chapters, we’ll discuss how to package, deploy, serve, and monitor the models in a robust manner.

In this chapter, we’ll cover:

• Model packaging formats

• Containerization

• Serving APIs

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 →

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!