Model Deployment in MLOps Lifecycle

...covered from a system design perspective.

Part 12 of the MLOps and LLMOps crash course is now available, where we continue our discussion on the deployment phase, diving deeper into important concepts, specifically Kubernetes (with implementations).

Read here: MLOps and LLMOps crash course Part 12 →

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:

• Images and containers basics

• Cloud-native and microservices architecture

• Kubernetes: Introduction and architecture

• Hands-on demo on using Kubernetes in MLOps

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 →

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!