Ray Train Specialization
The Ray Train learning path is a deep dive into distributed model training with Ray Train and gives you a large sample of interesting workloads to explore.
What you'll cover
- Level: Advanced
- Distributed Model Training
- Multi-GPU Training Workflows
- Reinforcement Learning Workloads
- Policy Gradient Methods
- Collaborative Filtering
Ray Train Specialization
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Your progress is tracked separately at the learning path level.
Course Sequence
7 steps
Model Training Foundations (Ray Train)
Foundations
Model Training Foundations (Ray Train)
Learn the foundations of distributed training of machine learning models with Ray Train.
Tabular Data Workload Example (Ray Train)
Workload Example
Tabular Data Workload Example (Ray Train)
A tabular data workload example built with Ray Train.
Time Series Data Workload (Ray Train)
Workload Example
Time Series Data Workload (Ray Train)
A time series data workload example built with Ray Train.
Computer Vision Workload Example (Ray Train)
Workload Example
Computer Vision Workload Example (Ray Train)
An example of distributed model training using Ray Train.
Generative Computer Vision Workload (Ray Train)
Workload Example
Generative Computer Vision Workload (Ray Train)
A generative computer vision workload example built with Ray Train.
Policy Learning Workload (Ray Train)
Workload Example
Policy Learning Workload (Ray Train)
A policy learning workload example utilizing Ray Train.
Recommender Systems Workload (Ray Train)
Workload Example
Recommender Systems Workload (Ray Train)
A recommender systems workload example built with Ray Train.