Public datasets and environments, end to end -- ingest with a real API key, query, and check the result yourself. Nothing here is a canned demo; every number is something you can recompute independently.
NASA's CMAPSS FD001 turbofan dataset with every sensor identifier stripped -- ingest it, query it, and reproduce the blind channel-classification result against your own independent check.
Bootstrap(api_key=...) ingest/queryUCI's Occupancy Detection dataset, with a real occupancy label on every row -- check whether the local channel classifier and outcome-tagged retrieval both agree with the ground truth.
No file to download -- a deterministic Gymnasium environment run on your own machine. Bias action choice toward what previously worked, then check that against a random-policy baseline and an independent local recomputation.
pip install gymnasiumThe practical follow-on to the Industrial whitepaper: connecting your own machine fleet -- including a resource-constrained ESP32 sensor node -- closing the outcome feedback loop, querying across devices, and sizing a deployment.
Prefer to just try it? Get an API key and query your first episode in under ten minutes.