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In enterprise production practice, if the built-in functions of the computing engine cannot meet user needs, users usually customize UDFs(User-Defined-Functions) through relevant interfaces and reference them in the engine. Especially for scenarios that require a lot of reuse, it is very important to effectively manage these custom functions. In Xiaomi's internal practice, there is also a UDF management system that basically meets user needs (limited to Spark/Flink, and there is no good support for AI + Python scenarios).
As a unified Data / AI metadata center, I think Gravitino can also support UDFs management in Data / AI scenarios.
The following are some commercial product practices:
Describe the feature
In enterprise production practice, if the built-in functions of the computing engine cannot meet user needs, users usually customize UDFs(User-Defined-Functions) through relevant interfaces and reference them in the engine. Especially for scenarios that require a lot of reuse, it is very important to effectively manage these custom functions. In Xiaomi's internal practice, there is also a UDF management system that basically meets user needs (limited to Spark/Flink, and there is no good support for AI + Python scenarios).
As a unified Data / AI metadata center, I think Gravitino can also support UDFs management in Data / AI scenarios.
The following are some commercial product practices:
Motivation
No response
Describe the solution
No response
Additional context
No response
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