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

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This is a syndicated post, view the original post here

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 look at the underpinnings behind machine learning model management and how it compares to database patching and upgrade.

What is Machine Learning Model Lifecycle Management?

A machine learning model includes features that define the model. An instance of the model is generated when the model is trained with available data for those features. An instance of the model must be deployed to its container before the model can be used to generate recommendations.

As newer training data becomes available, and the model is trained against that data, a new instance of the model becomes available. Now we have to manage multiple instances of models just like we managed multiple versions or releases of software. Newer versions (instances) of models must be evaluated against prior versions of models to determine if the newer version of the model will generate better recommendations. After all when you upgraded or patched software you were guaranteed that the updated version of software was an enhanced version of the software. Similarly with newer versions of models we must determine that it is better than the previous one. The outcome for recommendations is dependent on the dataset available for training the model.

Learn how machine learning model lifecycle management compares to database patch lifecycle management.

Oracle Adaptive Intelligent Best Candidates helps manage the model lifecycle so you don’t have to! We autonomously deploy the pre-trained Oracle model and use it for recommendations. Out of the box customers benefit from a model that is already trained, tested and deployed by Oracle. This enables quality recommendations in Oracle Adaptive Intelligent Best Candidates as soon as a new tenant is provisioned. For more on Adaptive Intelligence Candidate Matching, check out our docs here.

To learn more about AI for better business outcomes, visit the Oracle AI page. To learn more about innovative technologies for HR, check out the Oracle HCM page.

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