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Currently, we face the following weaknesses on OpenScale usage considering an MLOps pipeline automation use cases that require extensive use of OpenScale authoring API for automated monitoring of models within a deployment workflow:- Lack of proper documentation regarding authoring API. Since the Python SDK docs is the only available option it forces the client to reverse engineer it to be able to understand the underlying API model. - Such understanding becomes even more complex due to the large number of implementation entities exposed by the API, which requires a proper understanding of OpenScale implementation details that, again, becomes a hard task due to the lack of documentation. - Even the Python SDK documentation fails to be informative: there’s no description of the underlying types returned by each API endpoint. - There’s no clear description of how each underlying OpenScale entity exposed by the Python SDK relates to each other. This makes the construction of authoring workflows even harder.
In summary, the lack of proper authoring API documentation and the excessive exposition of underlying implementation level aspects becomes a significant obstacle to OpenScale adoption when considering use cases that require automation via API (for example, considering an MLOps pipeline).
What we suggest here is two-fold:
1) To properly expose authoring API as part of the current documented v2 API, altogether with clear and complete documentation. 2) To completely redefine the authoring API so that implementation details are hidden from users. In this case, high-level authoring operations would be readily provided by the API possibly under a transactional/asynchronous behavior, keeping the underlying complexity hidden (Façade API). This would substantially reduce the learning curve and potential usage errors.
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