Premise - usability of OSDU (open subsurface data universe).
Scenario - want to access seismic and well log data, prep ML.
Azure - Machine Learning Studio (classic) offers the best-in-class drag-and-drop machine learning builder. For those experienced in seismic data processing, the UI comes naturally. However, when it comes to OSDU, if accessible via OSDU connector, like Power BI, that will supercharge ML Studio to ML Nirvana. (Just add one OSDU box at the top to the rest of prep-ML workflow and done.)
GCP - once we extract and are happy with the column features, AutoML Tables is a powerful ML companion (no need to spin up a Jupyter Notebook). Caution - the new and heavily pushed Vertex AI offers similar treat BUT not the easy user-control turn on-off selected columns as in AutoML Tables. On-off test is important for understanding the value of information and benefit (add or subtract column features, which implies cost of acquiring additional data or redirecting funds for something else). And quantify delta in the resulting ML scores. OSDU support noted https://community.opengro...is/Releases/R2.0/GCP
AWS - while the latest OSDU API is available, the gap lies in the connector. Think HP printer driver. Had HP delivered the standard API, but each computer vendor had to provide the driver (for you and I to print), HP wouldn’t have sold as many cartridges. Wish list - AWS provides single-channel and multi-channel (prestack gathers) drivers in open-source repository (like Github). That will accelerate use and market adoption of OSDU. Not to mention unleashing the creative energy from AAPG-SEG-SPE members. Think how much that will boost the collective capability of delivering CCUS etc.
Overall - across all three cloud platforms, ML is made easier and usability increases each period.
Q: what is your perspective and experience on OSDU?