MLOps and AI in Production

40 hoursAdvanced5 modules

1 of 5 modules with a published syllabus

0 of 5 modules recorded

Syllabus

Deals with what happens after the model works: cloud for AI with GPU, storage and cost; observability and monitoring; serving at scale with batching, caching and autoscaling; training optimisation; and AI platform architecture.

Modules of this programme

The same objects as the full curriculum. A module opened from any programme that contains it is the same module, with the same syllabus and the same status.

Code	Module	Content	Hands-on	Status	Price
MLOPS-203	Cloud for AI (GPU, storage, cost)	Detailed syllabus not written yet	—	Syllabus published
CORE-303	Training optimisation	Mixed precision, schedulers, distributed basics	Make a training run 3× faster	Syllabus published	R$ 240US$ 80MX$ 1,000estimated price0 of 1 module published
MLOPS-301	Observability and monitoring	Detailed syllabus not written yet	—	Syllabus published
MLOPS-302	Serving at scale (batching, caching, autoscaling)	Detailed syllabus not written yet	—	Syllabus published
MLOPS-401	AI platform architecture	Detailed syllabus not written yet	—	Syllabus published

MLOPS-203Syllabus published
Cloud for AI (GPU, storage, cost)
Detailed syllabus not written yet
CORE-303Syllabus published
Training optimisation
Content
Mixed precision, schedulers, distributed basics
Hands-on
Make a training run 3× faster
R$ 240US$ 80MX$ 1,000estimated price0 of 1 module published
MLOPS-301Syllabus published
Observability and monitoring
Detailed syllabus not written yet
MLOPS-302Syllabus published
Serving at scale (batching, caching, autoscaling)
Detailed syllabus not written yet
MLOPS-401Syllabus published
AI platform architecture
Detailed syllabus not written yet

Not everything fits into 40 hours. State of the art, distributed training, interpretability, edge deployment and the five integrative projects exist only inside the complete tracks: Tracks