Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Watch now.
MLOps, a compound of “machine learning” and “information technology operations,” is a newer discipline involving collaboration between data scientists and IT professionals with the aim of productizing machine learning algorithms. The market for such solutions could grow from $350 million to $4 billion by 2025, according to Cognilytica. But certain challenges can make implementing MLOps a challenge. A survey by NewVantage Partners notes that only 15% of leading enterprises have deployed AI capabilities into production.
Validated and integrated with Nvidia AI Enterprise, Domino 5.0 brings capabilities to solve some of these challenges and make data science teams more productive. According to the company, it increases model velocity for data science teams (a metric of how fast they can build and update models) and dominates as the only platform that facilitates the end-to-end data science lifecycle.
Domino 5.0 Features
Among the new features, Domino brings the option of configuring Autoscaling Clusters, which will allow data scientists to spin up elastic compute clusters on-demand.
Intelligent Security Summit
Learn the critical role of AI & ML in cybersecurity and industry specific case studies on December 8. Register for your free pass today.
With support for open-source engines such as Ray, Dask, and Spark, this capability will let data scientists choose their preferred compute framework without locking them into a single option. Domino will dynamically grow and shrink the cluster based on workload demands, allowing more experimentation for better and faster model development while minimizing the compute cost and time wasted on DevOps work.
The platform will have Data Connectors, which will save data scientists from the trouble of finding and accessing data and configuring the right tools to connect to it. It streamlines the whole data connection process, allowing teams to securely share and reuse common data access patterns, removing a major speed bump in the research process.
In addition to connectors, Domino 5.0 introduces integrated monitoring with automated insights. When a model is being deployed, this feature will ensure that Domino automatically creates the pipeline to capture prediction data and compare it to training data to detect drift. In case the drift occurs, Domino offers a one-click option to let data scientists launch a development environment, with the original model materials, to investigate and redeploy. Automated insights simultaneously help data scientists rapidly diagnose drift by generating customized cohort analyses highlighting the likely causes behind the drift.
“Over the next decade, winning companies across industries will be the ones that weave data science into the fabric of their business and drive rapid continuous improvement of their models,” Nick Elprin, CEO and cofounder of Domino Data Lab, said. “Domino 5.0 gives enterprises the modern platform they need to maximize their model velocity and the impact of their data science investment.”
The update is available generally to existing Domino customers. And since it’s validated and integrated with Nvidia AI Enterprise platform, hundreds of thousands of companies already running VMware vSphere with Tanzu on NVIDIA-Certified Systems can cost-effectively build, deploy, manage, and scale accelerated ML workloads using Domino virtualized on industry-standard servers.
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.