MLOps / AIOps

MLOps & AIOps learning guide

This track is for learners who want to move beyond notebooks into deployment, monitoring, observability and automated model operations.

Best starting path

Begin with machine learning in production concepts before chasing fancy platform tools.
Google Cloud offers a path specifically focused on deploying and managing GenAI models and MLOps.
Use observability and incident thinking to understand the AIOps side.

Best paid or certification path

Coursera and DeepLearning.AI are strong for ML production foundations.
Google Cloud and Microsoft paths are useful when you want platform-specific credibility.
NVIDIA training is useful if you want deeper GenAI deployment and performance knowledge.

Recommended sites

Site	Why use it	Best for
Google Cloud Skills Boost	MLOps for generative AI and Vertex AI oriented workflows.	Cloud MLOps
Coursera / DeepLearning.AI	Clear production ML foundation from a respected source.	MLOps beginners
Microsoft Learn	GenAIOps lifecycle with GitHub and Azure-flavoured workflows.	GenAIOps learners
NVIDIA DLI	Training for RAG pipelines, LLM deployment and performance topics.	Advanced deployment focus

How to save money

Study the free theory first, then pay only for hands-on labs you will actually complete.
Prefer programs with labs or portfolios over certificate-only marketing.
Use vendor training when your job target is cloud-specific.

What to build after learning

A model monitoring dashboard mock project.
An end-to-end pipeline with data, training, deployment and drift notes.
An AIOps incident summarizer using logs and alert trends.