AI Infrastructure and MLOps Courses
9 courses on the systems that keep AI running in production — deploying models, scaling them, monitoring their behavior, and managing the data and pipelines behind them. They're for engineers who run AI systems in production: making them reliable, observable, and maintainable.
These courses cover the infrastructure and operational practices — often called MLOps — that turn a one-off model into a dependable service: packaging and deploying it, serving predictions at scale, versioning models and data, watching for drift and degradation, and automating the pipeline so retraining and redeployment run without manual steps. They're aimed at engineers, platform teams, and anyone responsible for AI systems in production rather than in a notebook. If your concern is uptime, cost, and reproducibility as much as accuracy, this is the material for you.
AI Infrastructure & MLOps courses
9 courses.
Advanced Machine Learning Operations
Databricks
Building Language Models on AWS
AWS Skill Builder
Google Cloud AI Infrastructure
Google Skills
Machine Learning at Scale
Databricks
Machine Learning Model Deployment
Databricks
Operationalize generative AI applications (GenAIOps)
Microsoft Learn
Operationalize machine learning models (MLOps)
Microsoft Learn
Professional Machine Learning Engineer Certification
Google Skills
The Art of Compressing LLMs: Pruning, Distillation, and Quantization
NVIDIA Deep Learning Institute
Frequently asked questions
- What is MLOps?
- MLOps is the set of practices for deploying, monitoring, and maintaining machine learning models in production — bringing software-engineering discipline like automation, versioning, and observability to AI systems so they stay reliable over time.
- Who are these AI infrastructure courses for?
- Engineers and platform teams responsible for running AI systems in production — deploying models, scaling them, and keeping them reliable — rather than for people focused only on training models.
- Do I need to be able to train models first?
- A working understanding of how models are built helps. Most of these courses focus on deployment and operations — infrastructure, pipelines, and tooling — and a few, such as Building Language Models on AWS, also cover training models at scale.
Key concepts
The foundational terms these courses build on — each chip links to a plain-English definition in the AI Pinnacle glossary.