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Domino Data Lab, maker of an end-to-end MLops (machine learning operations) platform, is announcing its latest release — version 5.3 — today. The delivery includes new support for ML model inferencing on GPU (graphics processing unit) systems and a collection of new connectors. Along with that, the company is beginning a private preview of its Nexus hybrid and multicloud capabilities, first announced in June.
[Also read: Domino Data Lab announces latest MLops platform to satisfy both data science and IT]
GPUs accelerate deep learning
GPUs can make lots of ML and deep learning operations go faster because they parallelize massive workloads, which is exactly what training complex deep learning models or numerous ML models entails. For this reason, Domino has long supported GPUs for model training.
But in the case of deep learning specifically, GPUs can benefit inferencing (generating predictions from the trained model) as well, and it is this scenario that Domino newly supports in version 5.3. Perhaps an easier way of thinking about this is that Domino now supports operationalization of deep learning beyond development, extending into production deployment. Given all the new announcements that came out of Nvidia‘s GPU Technology Conference (GTC) last month, Domino’s timing here is especially good.
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[See also: Nvidia moves Hopper GPUs for AI into full production]
Connectivity to platforms old and new
Then there’s the matter of new connectors, including enhanced connectivity for Amazon Web Services S3 and brand new connectors for Teradata and Trino. Usually, new connectors are not newsworthy … delivery of them is just a typical, incremental enhancement that most data platforms add at regular intervals. But there are a couple of tidbits here that are worth pointing out.
Coverage of a mature, well-established data warehouse platform like Teradata shows a maturation in MLops itself. Because MLops platforms are new, they often prioritize connectivity to newer data platforms, like Snowflake, for which Domino already had support. But adding a Teradata connector means MLops and Domino are addressing even the most conservative enterprise accounts, where the impact of artificial intelligence (AI) will arguably have the biggest, even if not the earliest, impact. It’s good to see the rigor of MLops make its way around all parts of the market.
[Must read: Teradata takes on Snowflake and Databricks with cloud-native platform]
Connecting to Trino — an open-source federated query engine derived from Presto development work at Facebook — is important in a different way. Connecting to Trino provides further connectivity to all of its target data platforms, including NoSQL databases like MongoDB and Apache Cassandra, data lake standards like Delta Lake and Apache Iceberg, streaming data platforms like Apache Kafka, analytics stores like Apache Druid and ClickHouse, and even productivity data sources like Google Sheets.
[Check out: MongoDB fires up new cloud, on-premises releases]
Private preview for hybrid and multicloud
Finally, there’s the Domino’s Nexus hybrid/multicloud capabilities, which allow Domino to deploy model training environments across on-premises infrastructure and the three major public clouds, with costing information for each, all from a proverbial single pane of glass. This is pictured in the figure at the top of this post. And because Nexus works across cloud regions, it can also support restricting access to data by geography, to enforce data sovereignty policies and comply with corresponding regulations.
At this time, Nexus is available only to participants in Domino’s private preview for same. But progress is progress. Private previews advance to public previews, and public previews eventually progress to general availability (GA). Speaking of GA, Domino 5.3 is generally available now, according to the company. And customers interested can sign up for the Nexus private preview.