Distributed AI systems enablement for Python teams
Practical curricula for Ray Core, Ray Data, Ray Train, Ray Tune, Ray Serve, and RLlib.
Nora Singh
Distributed AI Platform Engineer
Active courses
4
Three courses and one certificate are in progress.
Completion
56%
Progress combines assigned coursework with recent activity.
Team rank
#4
Based on current academy standings.
Featured curriculum
A focused catalog for technical enablement teams.
Ray Core for Python Systems
Build the mental model for Ray tasks, actors, object references, scheduling, and resource-aware distributed Python.
Ray Data for Batch AI
Design Ray Data pipelines for parallel ingest, preprocessing, offline inference, and reliable outputs.
Ray Train and Tune for ML Teams
Coordinate distributed training jobs and credible hyperparameter experiments with Ray Train and Ray Tune.
Ray Platform Engineer
Build and operate distributed Python applications with Ray Core, Data, Serve, and production readiness practices.
Open path
Ray ML Engineer
Scale training, tuning, batch inference, and online serving with clear model lifecycle handoffs.
Open path
Ray Applied AI Researcher
Explore RLlib, distributed experiments, and Serve-based application patterns for research teams moving toward production.
Open path