Autonomous: When Database Patch Lifecycle Management meets Machine Learning Model Lifecycle …

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By Sonali Inamdar, Director of Software Development

If you are a long time Oracle customer, you may recall the days in 2009 when Oracle 11g along with Oracle Enterprise Manager 11g was released. Oracle Enterprise Manager Patching and Provisioning Pack 11g included capabilities for automated database provisioning, patching and upgrade. Compliance for Oracle recommended Critical Patch Updates (security patches) was achieved when Patch Advisories in Enterprise Manager first discovered patch drift and then provided automated mechanisms (deployment procedures) to correct the drift.

A decade later, Oracle has transformed. Oracle’s Cloud strategy and unmatched breadth of business applications provides a unique opportunity for utilizing data to make AI and Machine Learning fulfill its promise. Oracle provides innovative solutions using Machine Learning with its Adaptive Intelligent Applications portfolio which uses machine learning and AI in real time to produce better business outcomes.

While machine learning provides plenty of opportunities for innovation, it also brings its own challenges, particularly within model lifecycle management. After working with both Enterprise Manager and Adaptive Intelligent Applications here at Oracle, I’ve found that machine learning model lifecycle management and database patch lifecycle management face similar challenges – and Oracle has worked to provide solutions for both. Let’s

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