Abstract: The most popular term in machine learning and data science in recent times is probably “MLOps”; it has been used to describe everything and nothing about the process of creating and deploying models. However, the fact of the matter is that building AI-enabled products remains a major challenge and requires more than just software or model development practices; it requires a unique combination of practices from software development, ML development, and data management. Loosely termed DevOps, MLOps, and DataOps respectively, only together can these three areas enable rapid development of intelligent products.
Drawing upon experiences bringing dozens of AI-enabled products to market, this talk will discuss what it takes to ship AI-enabled products; demystify the terms DataOps, MLOps, DevOps; and provide best practices for structuring teams to support intelligent software development.
Bio: Manasi Vartak is the founder and CEO of Verta, a Palo Alto-based startup building tools for AI & ML model management and operations. Manasi is the creator of ModelDB, the first open-source model management system deployed at Fortune-500 companies. She previously worked on deep learning for content recommendation as part of the feed-ranking team at Twitter and dynamic ad-targeting at Google. Manasi is passionate about building intuitive data tools, helping companies become AI-first, and figuring out how data scientists and the organizations they support can be more effective. Manasi has spoken at several top research and industry conferences such as SIGMOD, VLDB, SparkSummit, TWIML, Data Science Salon, and AnacondaCon, and has authored a course on model management. Manasi earned her MS/PhD in Computer Science from MIT.