Foundations of AI Engineering and LLMOps

The full LLMOps blueprint (with code).

Last week, we concluded the MLOps crash course with 18 parts.

Now we are moving to the full LLMOps crash course, and Part 1 is now available, which covers:

• Fundamentals of AI engineering & LLMs

• The shift from traditional ML models to foundation model engineering

• Levers of AI engineering

• MLOps vs. LLMOps key differences

While learning MLOps, we primarily explored traditional ML models and systems and learned how to take them from experimentation to production using the principles of MLOps.

But what happens when the “model” is no longer a custom-trained classifier, but a massive foundation model like Llama, GPT, or Claude?

Are the same principles enough?

Not quite.

Modern AI applications are increasingly powered by LLMs, which introduce an entirely new set of engineering challenges that traditional MLOps does not fully address.

This is where LLMOps come in.

It involves specialized practices for managing and maintaining LLMs and LLM-based applications in production, ensuring they remain reliable, accurate, secure, and cost-effective.

The aim is to provide you with a thorough explanation and systems-level thinking to build LLM apps for production settings.

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

As we progress, we will see how we can develop the critical thinking required for taking our applications to the next stage and what exactly the framework should be for that.

Read Part 1 on fundamentals of LLMOps here →

👉 Over to you: What would you like to learn in the LLMOps crash course?

Thanks for reading!