Abstract: 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.
Bio: 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