Streamlining MLOps from Experiment to Production
The value of machine learning (ML) is largely unrealized, 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.
Watch this webinar to see demos of 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 centralized model governance and monitoring