The value of machine learning (ML) is largely unrealised, as most models are never deployed in production. This disconnect between experiments and production can be attributed to siloed data, tools, and teams that are unable to come together to deliver on ML’s proven value across industries and use cases. By adopting cohesive platforms that enhance collaboration and streamline the development, deployment, and monitoring of both data pipelines and models, teams can establish robust machine learning operations (MLOps) practices to bring their models into production.

Join this Live Demo on 8 September to learn how Snowflake and Dataiku can:

  • 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 centralised model governance and monitoring

In partnership with:


  • Prash Medirattaa

    Partner Sales Engineer - AI & ML, Snowflake

  • Chase Ginther

    Data Platform Architect, Snowflake

  • Stephen Franks

    Senior Solutions Architect, Dataiku

  • Dan Darnell

    Head of Product Marketing, Dataiku