Domino 5.2 Brings Efficient Data Science Across the MLOps Lifecycle

Domino Data Lab, provider of a leading enterprise MLOps platform is introducing Domino 5.2, continuing Domino’s progress towards helping enterprises become model-driven.

Throughout the lifecycle of a model, data science teams must contend with non-data science tasks, workarounds caused by inflexible tools and complex processes, as well as rising tensions with IT. Domino 5.2 helps reduce this complexity and alleviate friction with IT over costs—allowing teams to accelerate the MLOps lifecycle, according to the vendor.

New capabilities will recommend the optimal size for a development environment, thereby improving the model development experience for data science teams and delighting IT with reduced cloud storage costs.

Integrated workflows in Domino 5.2 automate model deployment to Snowflake’s Data Cloud and enable the power of in-database computation, as well as model monitoring and continuous identification of new production data to update data drift and model quality calculations that drive faster and better business decisions.

“Data science teams make their biggest impact on business and humanity when they focus on building, deploying and improving models on their own terms, without fear of vendor lock-in, rising costs or risk,” said Nick Elprin, CEO and co-founder of Domino Data Lab. “Domino 5.2 enables our customers to maximize model velocity across the MLOps lifecycle using powerful tools of their choice and scalable infrastructure like the Snowflake Data Cloud.”

It is widely acknowledged that data scientists spend significant time building, preparing, and cleansing data before modeling. With Domino 5.2, they can now overcome this hurdle using a native SQL-based environment based on Apache Superset. This fully integrated option for data visualization delivers to teams a deeper understanding of the breadth and quality of their data to be modeled.

Orchestrating the movement of data takes custom development work and forces manual workarounds that consume valuable time for data scientists and ML engineers, and introduce unnecessary risk.

Domino has partnered with Snowflake to integrate end-to-end workflows across the MLOps lifecycle. Domino 5.2 combines the flexibility of model building in Domino with the scalability and power of Snowflake’s platform for in-database computation.

Customers can train models in-database using Snowflake’s Snowpark, then deploy those models directly from Domino to the Snowflake Data Cloud for in-database scoring—simplifying enterprise infrastructure with a common data and deployment platform across IT and data science teams.

With Domino 5.2, data science teams can now automatically set up prediction data capture pipelines and monitoring for models deployed to the Snowflake Data Cloud. Domino will also now continuously update data drift and model quality calculations to drive increased model accuracy and ultimately better business decisions.

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