[Suggestion]: Ray use cases section should split simple scaling vs advanced use cases

[ericl]

Please share your suggestion here

Currently the list of use cases in https://github.com/ray-project/ray-educational-materials/blob/main/Introductory_modules/Overview_of_Ray.ipynb contains the following:

• Exoshuffle
• Building a custom feature engineering library
• Alpa
• RLlib / FIFA
• Multi-model serving
• RL training / Riot
• ML platform / Shopify
• ML platform / Spotify

This is skewed toward advanced use cases, which I don't think accurately reflects the entire target audience of Ray. I think it would be productive to break this down into two categories:

• Scaling simple ML workloads
  • Batch inference on CPUs and GPUs (Core / Data)
  • Parallel training of many small models / Distributed training of large models (Core / Train)
  • Managing parallel experiments and hyperparameter tuning (Tune)
  • Serving model pipelines or multiple models (Serve)
  • Reinforcement Learning (RLlib)
  • ML platform use cases (Shopify, Spotify)
• Implementing advanced ML workloads
  • Alpha
  • Exoshuffle
  • Custom feature eng library
  • RL training / Riot / FIFA

[kamil-kaczmarek]

thanks @ericl this is very helpful suggestion.

[kamil-kaczmarek]

@emmyscode it is related to the #15