Agenda: 6:00 - 6:30 pm: Social Hour with Food, Drinks, Beer & Wine 6:30 - 6:35 pm: Introduction & Announcements 6:35 - 7:05 pm: Talk 1 Managing the Full Deployment Lifecycle of Models with the MLflow Model Registry (Databricks) 7:05 - 7:35 pm: Talk 2 MLflow on and inside Azure (Microsoft) 7:35 - 8:05 pm: Talk 3 TensorFlow(X) Data Validation: Better ML through better data (Google) 8:05 - 8:30 pm: Additional Networking Talk 1 - Title: Managing the Full Deployment Lifecycle of Models with the MLflow Model Registry Presenter: Mani Parkhe, Databricks Abstract: MLflow is an open-source platform to manage the machine learning lifecycle, including experiment tracking, reproducible runs, and model packaging. In this talk, we provide an overview of the latest component of MLflow, the Model Registry, which serves as a collaborative hub where teams can share, discuss, use, inspect, and track the lineage of models. Model Registry was introduced in MLflow 1.4 and is in Private Preview on Databricks With this addition, MLflow provides end-to-end management of the deployment lifecycle of models from experimentation to online testing and production, complete with approval and governance workflows. Bio: Mani Parkhe is an ML/AI Platform Engineer at Databricks, focusing on the customer and open-source platform initiatives, which enable data discovery, training, experimentation, and deployment of ML models on the cloud. After spending 15 years building software for semiconductor chip CAD, Mani transitioned to building big data infrastructure, distributed systems and web services, and machine learning platforms.