In this lesson, you will learn what MLOps is and why it is essential for scaling machine learning systems. We will explore how MLOps extends DevOps to manage not just code, but also data, models, and experiments, and compare the DevOps and MLOps lifecycles.
You will also get a high-level view of MLOps architecture, see how teams progress through maturity levels, and cover the MLOps tooling landscape from foundations to serving, deployment, and cloud platforms.
Along the way, we will include a short Linux basics primer (most modern MLOps/MLflow workflows run on Linux) to prepare readers for hands-on work in future lessons.
Finally, we will touch on LLMOps to understand what it is, how it relates to MLOps, and why it is becoming critical in the era of large language models.
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