In today's economic environment, all organizations need to unlock greater AI value, faster. 98% of CDOs and CDAOs say the companies that bring AI and ML solutions to market fastest will be the ones who survive and thrive in the upcoming times of economic uncertainty. More than ever, organizations need to scale the creation and operationalization of ML models across their businesses and bring to bear the latest, most powerful AI methods and tools.
To help organizations accomplish this, we are proud to announce the general availability (GA) of Domino’s Spring 2023 release. This release helps organizations grow data science talent, seamlessly develop and operationalize models at scale across any environment, enhance collaboration and governance, and accelerate business impact using the latest data science methods.
Particularly notable GA features include Domino’s hybrid- and multi-cloud Nexus capability and Domino Cloud – a new, fully-managed MLOps platform-as-a-service for fast and easy data science at scale. Further, the release expands enterprise-grade support for the open-source ML tools — Ray 2.0, MLflow, and FEAST— used to develop today’s most advanced AI applications.
Tear down AI silos across cloud and on-prem with hybrid and multicloud Nexus
Organizations need the power of hybrid and multi-cloud to develop and deliver AI solutions everywhere the business operates and to take advantage of the unique advantages each environment provides. Announced in June 2022, Domino Nexus is now generally available and provides a single pane of glass that lets you run data science and ML workloads across any compute cluster — in any cloud, region, or on-premises. It unifies data science silos across the enterprise, so you have one place to build, deploy, and monitor models. Domino Nexus helps companies:
Protect data sovereignty. Domino Nexus allows you to move compute to your data, minimizing the time and costs of data transfers across regions. It helps you comply with data sovereignty laws by allowing you to restrict access to data by region.
Reduce compute spend. By providing on-demand access to on-prem and cloud infrastructure, Domino Nexus helps you get the right performance at the lowest possible cost, e.g. reduce cloud infrastructure costs for computationally intensive ML workloads by increasing utilization of on-premises infrastructure
Streamline migration and future-proof investment. Nexus helps you implement your cloud strategy, whether that be migrating to the cloud or a strategy that is hybrid or multi-cloud by design. The unified data science platform helps you mitigate cloud vendor lock-in and outage risks and ensure that you can always provide the best tools and infrastructure while maintaining consistent operations.
Get data science up and running immediately with Domino Cloud
New data science teams need to deliver value fast, and they need access to professional-grade tools and infrastructure even faster. As part of the Spring 2023 release, Domino is launching Domino Cloud, a fully-managed version of its MLOps platform delivered as SaaS. With Domino Cloud customers can:
Deliver immediate access. Domino Cloud accelerates AI time-to-value by providing full access to the complete ecosystem of professional data science tools and scalable infrastructure they need to drive immediate business impact. This means your team can spend more time on data science rather than waiting on other teams to deploy tools and provision infrastructure.
Optimize TCO. Customers can maximize the impact of limited manpower and budget. Domino Cloud eliminates the need for data science teams to worry about supporting the growing ecosystem of data science tools or deploying, upgrading or managing infrastructure, allowing them to focus on their core responsibilities. Teams can save costs by paying only for the compute your team uses while still getting access to scalable compute including GPUs, and distributed compute frameworks.
Accelerate AI innovation and impact with cutting-edge, scalable ML technologies
To create sustained competitive advantage, organizations need to move beyond the standard set of ML methods to pursue new use cases and ever-increasing levels of accuracy. They need to pursue the latest AI use cases that involve complex training and exceptionally large amounts of data, and infrastructure.
Domino’s Spring 2023 release provides a host of new capabilities that help enterprises experiment with, develop and deploy even the most cutting-edge ML models. Chief among these is the ability to:
Train models at Generative AI scale with Ray 2.0. Domino now supports version 2.0 of the Ray open-source framework, which enables data science teams to rapidly train even the largest, most compute-intensive models, such as Generative AI models like ChatGPT. Domino's on-demand, auto-scaling compute clusters automate the DevOps of provisioning and managing Ray clusters. They provide on-demand access making data scientists more productive, but also cut waste by reducing the time infrastructure sits idle.
Manage experimentation at scale with MLflow. Domino now provides MLflow out-of-the-box enabling data scientists to use this industry-standard tool for ML model lifecycle management. It makes it easy for data scientists to track, reproduce, and share machine learning experiments and artifacts within their Domino projects, while Domino's security layer ensures metrics, logs, and artifacts are secured.
Domino's integration with MLflow simplifies machine learning lifecycle management for data scientists.
Manage, re-use and reproduce features at scale with Feast. Domino now integrates natively with Feast, the most popular open-source feature store for machine learning, providing users with easy access to query and transform ML features. This integration allows teams to reuse feature logic consistently and efficiently across data science projects, while tracking feature lineage and ensuring data accuracy and security, as well as cost savings from not re-computing business logic for each feature.
Feast integration allows teams to reuse feature logic consistently and efficiently across data science projects
Accelerate cutting-edge AI impact at scale and boost time-to-value with managed AI
Domino’s 2023 spring release helps enterprises grow analytics and data science talent, develop and operationalize AI applications across hybrid and multi-cloud environments, get up and running faster using a managed platform, and increase adoption of the latest data science methods and tools. However, this is only a subset of the new capabilities included in this release. It also includes new capabilities for measuring the performance of models, inference deep learning models, and audit trails, to name but a few. To learn more, please visit our documentation site and launch website. Also, follow us on LinkedIn to get further updates and details about our upcoming Rev 4 data science conference in New York City on June 1-2nd that you won’t want to miss.
Kjell Carlsson is the head of AI strategy at Domino Data Lab where he advises organizations on scaling impact with AI technologies. Previously, he covered AI, ML, and data science as a Principal Analyst at Forrester Research. He has written dozens of reports on AI topics ranging from computer vision, MLOps, AutoML, and conversation intelligence to augmented intelligence, next-generation AI technologies, and data science best practices. He has spoken in countless keynotes, panels, and webinars, and is frequently quoted in the media. Dr. Carlsson is also the host of the Data Science Leaders podcast and received his Ph.D. from Harvard University.
Domino Platform
The enterprise platform to build, deliver, and govern AI
Watch the 15 minute on-demand demo to get an overview of the Domino Enterprise AI Platform.
