Despite the attention and sizable investments, many companies still struggle to expand data science projects within their organizations. Only a small set of companies in the market have successfully implemented Machine Learning- AI projects and took them from experimental stages to production at scale.

It’s critical operationalizing machine learning efficiently and effectively. So how do we bridge the gap? A data scientist and an engineer will approach the problem of building, unifying, and scaling these algorithms. With our research and experience with customer's ML projects, we have learned about the significant challenges enterprises face. In this session, we’ll propose solutions to address these obstacles. With detailed talk about architecture for successful AI/ML deployment.