ODSC APAC 2021: Re-Trainable MLOps Systems
Learn about MLOps service architecture that can automatically add the data, auto annotate, and retrain in a way that the accuracy is improved with AutoML technology.
Session Overview
The original MLOps process is to build a machine learning pipeline that can be retrained after data management → artificial intelligence development → deployment. But most libraries and paid tools are only capable of machine learning pipelines that cannot be retrained. So, we introduce the MLOps service architecture that can automatically add the data, auto annotate, and retrain in a way that the accuracy is improved with AutoML technology.
Overview
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Re-Trainable MLOps Systems
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Abstract and Bio
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Re-Trainable MLOps Systems
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Instructor
Technical Sales Specialist | DSLAB Global
Marcus Kim
Marcus is the Global Technical Sales Specialist for DSLAB GLOBAL. Previously, he worked as a business analyst for a Seoul-based Ed-tech company and sales manager for State Farm Insurance & Financial Services. He has a BS in Biology/Chemistry from Virginia Commonwealth University.