The Full MLOps/LLMOps Blueprint
...covered with foundations, projects, and real-world insights.
After the MCP, RAG, and AI Agents crash course, we have started the much-awaited series on MLOps, which will cover LLMOps as well.
Just like all our past series, this series is both foundational and implementation-heavy, walking you through everything that a real-world ML system entails:
In Part 1, we cover the foundations:
Why does MLOps matter?
MLOps vs. DevOps and traditional software systems
System-level concerns in production ML
The ML system lifecycle.
And how MCP solves it through a structured Host–Client–Server model.
In Part 2, we go hands-on and cover:
The entire ML system lifecycle.
Data pipelines
Model training and experimentation
Model deployment and inference
Model deployment and inference
Hands-on project from training to API
Why care?
What happens once you have trained your ML model and tested its inference capabilities?
Is the job done?
Not really.
What you’ve completed is only a small part of a much larger journey that will unfold next.
If you plan to deploy the model in a real-world application, there are many additional steps to consider. This is where MLOps becomes essential, helping you transition from model development to a production-ready system.
It’s where ML meets software engineering, DevOps, and data engineering.
The goal is to reliably deliver ML-driven features (like recommendation engines, fraud detectors, voice assistants, etc.) to end-users at scale.
Hence, as mentioned earlier, a key realization is that the only a tiny fraction of an “ML system” is the ML code; the vast surrounding infrastructure (for data, configuration, automation, serving, monitoring, etc.) is much larger and more complex:
MLOps seeks to manage this complexity by applying reliable software engineering and DevOps practices to ML systems, ensuring that all these components work in concert to deliver value.
This MLOps and LLMOps crash course will provide you with a thorough explanation and systems-level thinking to build AI models for production settings.
Just as the MCP crash course, each chapter will clearly explain necessary concepts, provide examples, diagrams, and implementations.
Thanks for reading!