Domino Data Lab Unveils Platform to Accelerate Model Velocity for the Model-Driven Business
Domino 5.0 introduces new capabilities to help enterprises accelerate data science at scale.
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Domino Data Lab, provider of an enterprise MLOps platform trusted by over 20 percent of the Fortune 100, today introduced Domino 5.0 with new capabilities that improve model velocity (a metric of how fast data science teams build and update models) by solving common challenges related to computing infrastructure, data, and productionization of models.
As companies invest in data science and machine learning, many fail to achieve the impact they expect because existing processes, cultures, and technologies make it hard for data scientists to rapidly and safely develop and deliver models. New capabilities that unify model development, deployment, and monitoring help Domino 5.0 facilitate the end-to-end data science life cycle while giving data scientists the flexibility to use their preferred tools. By making data scientists more productive and increasing collaboration and reuse of work, Domino 5.0 improves model velocity for data science teams.
“Through our best practices and use of Domino’s Enterprise MLOps platform, we’ve been able to accelerate model deployment by as much as six times,” said Jacob Grotta, general manager of banking operating unit, Moody's Analytics. “This increase in model velocity significantly improves our ability to get information into the hands of our clients faster and solve their challenges in ways that would previously have been impossible.”
New Capabilities
Domino 5.0 introduces three capabilities that address common challenges data science teams face: accessing computing infrastructure, collaborating using data sources, and productionizing models.
- Autoscaling clusters let data scientists spin up elastic compute clusters on demand with just a few clicks. With support for Ray, Dask, and Spark, Domino lets data scientists choose their preferred computing framework without locking them into a single option. Domino will dynamically grow and shrink the cluster based on workload demands. This allows more experimentation to accelerate innovation while minimizing compute cost and saving data scientists from wasting precious time on DevOps work.
- Data connectors eliminate significant time wasted by data scientists finding and accessing data, including configuring the right tools to connect to it. Domino 5.0 streamlines that entire process, allowing data science teams to securely share and reuse common data access patterns, removing a major speed bump in the research process.
- Integrated monitoring with automated insights unifies model development, deployment, and monitoring to speed up the process of continuously improving models. When deploying a model, Domino automatically creates the pipeline to capture prediction data and compare it to training data to detect drift. When drift occurs, Domino lets data scientists easily launch a development environment with the original model materials to investigate and redeploy it. Additionally, automated insights help data scientists rapidly diagnose drift by generating customized cohort analyses that highlight likely causes in an easy-to-consume report.
Domino 5.0 is available to existing Domino customers immediately. New users who would like to try Domino can do so at dominodatalab.com/trial.