Streamlining MLOps from Experiment to Production with Snowflake and Dataiku

The value of machine learning (ML) is largely unrealized, as most models are never deployed in production. Teams who adopt cohesive platforms that enhance collaboration and streamline the development, deployment, and monitoring of both data pipelines and models, can establish robust machine learning operations (MLOps) practices to bring their models into production. Webinar [Dataiku.jpg]() Hi there, The value of machine learning (ML) is largely unrealized, as most models are never deployed in production. Teams who adopt cohesive platforms that enhance collaboration and streamline the development, deployment, and monitoring of both data pipelines and models, can establish robust machine learning operations (MLOps) practices to bring their models into production. [Register for this upcoming webinar]() to learn how to: - Increase speed of development with efficient data processing with Dataiku pushdown to Snowflake’s compute engine - Streamline path to production with Dataiku ML models deployed as Snowpark for user-defined functions (UDFs) - Help MLOps teams regain control of model proliferation with centralized model governance and monitoring [Register Now]() Optimize the Machine Learning Lifecycle and MLOps Download this report to learn how a cloud data platform enables teams to standardize and manage the ML lifecycle to help organizations achieve the scalability, reproducibility, and governance they need to succeed with machine learning. [Register Now]() Suite 3A, 106 East Babcock Street, Bozeman, Montana 59715 You’re receiving this email because you’re signed up to receive updates from Snowflake. If you’d prefer not to receive updates, you can [unsubscribe](.