A Unified and User Friendly Approach to Develop ML Solutions in MySQL HeatWave AutoML


We discuss a unified and user friendly approach to develop ML applications on MySQL HeatWave AutoML. We describe a simple to use MySQL API for HeatWave AutoML, its extension for different use cases and its ability to interface with third party applications. We also discuss how this facilitates the development of classification, regression, anomaly detection, forecasting and recommendation systems use cases, and present its comparison with other platforms that offer ML features for data bases. Lastly, we present how a user can fine tune the AutoML pipeline for their needs.

Background Knowledge:

General familiarity with Machine Learning


Sanjay Jinturkar is Senior Director, MySQL HeatWave, focused on building machine learning capabilities inside the MySQL HeatWave database. These capabilities enable the user to automatically develop and deploy machine learning models inside the data base using AutoML in a cloud native environment for a variety of use cases, without the need to pull the data or the model outside the database. In past, he has held multiple technology and engineering management positions in systems software, mobile communications software, applications development and diagnostics. His interests include machine learning, cloud computing, database and architecture. Sanjay has a PhD in Computer Science from University of Virginia.

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