New capabilities accelerate data preparation, model development, and enable model deployment in Snowflake’s Data Cloud

Today at the Rev 3 conference, Domino Data Lab, provider of the leading Enterprise MLOps platform trusted by over 20% of the Fortune 100, announced its latest platform, Domino 5.2. This powerful release continues Domino’s progress towards helping enterprises become model-driven by allowing data science and IT teams to develop and deploy more models faster, slash data and infrastructure complexity and costs, and extend autonomous model performance monitoring to Snowflake’s Data Cloud.

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.

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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.”

“Our teams demanded a machine learning platform that would give us the freedom to leverage a variety of data platforms while managing cost and risk,” said Stig Pedersen, head of machine learning at Topdanmark. “Domino’s Snowflake Data Cloud integration helps our team focus on data science, not complicated data regulatory requirements, with improved efficiencies, such as rapidly discovering model drift to minimize the potential business impact of suboptimal predictions.”

With powerful capabilities across each critical stage of the MLOps lifecycle, this release solidifies Domino’s position as the most open and flexible Enterprise MLOps platform in the industry:

Smarter model development with a new data preparation and visualization option, and IntelliSize for development environments
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.

As data scientists use more software and compute, IT needs to manage more cost and operational burden. Domino’s Durable Workspace development environments are now smarter and more efficient with a new IntelliSize capability that will recommend the optimal size for an environment. For data science teams, this means less complexity and more productivity. For IT, eliminating excess workspace capacity and automatically deleting abandoned workspaces can significantly reduce monthly cloud storage costs.

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Flexible model deployment using Snowflake for in-database computation
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.

Streamlined real-time model monitoring in Snowflake Data Cloud Environments
Models most often decay as soon as they are put into production due to changes in business conditions, customer preferences, and other factors. Without proactive monitoring for data drift and accuracy, companies risk making bad business decisions based on outdated models. 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.