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Domino Data Lab Expands Collaboration with NVIDIA and TCS with New Enterprise MLOps Solutions for Modern IT Stacks

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New flexible deployment and procurement options to unite analytics and AI workloads with high-performance computing in a single solution and help democratize AI for enterprises worldwide

Domino Data Lab, provider of the leading Enterprise MLOps platform trusted by over 20% of the Fortune 100, at GTC announced a new fully managed offering from solution partner Tata Consultancy Services (TCS) that leverages the Domino platform to easily and cost-effectively run high-performance computing and data science workloads on NVIDIA DGXTM systems, all in the TCS Enterprise Cloud. Along with the coming integration of Domino’s platform with the NVIDIA AI Enterprise software suite, this represents a pivotal next phase of collaboration between Domino, NVIDIA and TCS to help make every company model-driven.

“Enterprises increasingly use machine learning to solve the world’s most important problems,” said Nick Elprin, CEO and co-founder of Domino Data Lab. “Domino’s Enterprise MLOps Platform on NVIDIA’s accelerated computing infrastructure will enable our customers to accelerate breakthrough research and increase the productivity of their data science teams.”PREDICTIONS SERIES 2022

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Domino’s integrations with NVIDIA technologies have enabled many of the world’s largest companies to build competitive advantage using GPU-based training for increasingly sophisticated models. Current use cases range from anomaly detection for manufacturing and supply chains to predictive maintenance for infrastructure in energy and utilities. Since 2020, Domino has offered certified Enterprise MLOps software for NVIDIA DGX systems as a charter member of the NVIDIA DGX-Ready Software program, working with partners such as NetApp to produce pre-configured and validated hardware and software package bundles.

“NVIDIA DGX systems optimized with an MLOps platform can help increase success, drive utilization of resources and improve time-to-value for data science projects,” said David Menninger, SVP and Research Director at Ventana Research. “An MLOps platform such as Domino is critical for quickly governing, developing, and deploying AI models in production applications.”

Full-Stack Enterprise MLOps as a Managed Service

Available now, TCS’s High Performance Computing Accelerated AI & Analytics (HPC A3) solution on the TCS Enterprise Cloud provides a single, converged end-to-end solution for training AI, ML and deep learning models using Domino and NVIDIA DGX systems in the same heterogeneous compute environment as HPC simulation workloads.

Using HPC A3, data scientists can leverage output data from CPU-accelerated containerized simulation workloads to train NVIDIA GPU-accelerated ML models, or vice versa, without having to move data across two traditionally siloed environments. With a single pane of glass, data science leaders can use this solution to track project status across teams, while IT can track infrastructure utilization for capacity planning.

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“The most difficult challenges in industries like life sciences and manufacturing can be solved by efficiently leveraging the proliferation of data from connected devices, and by converging simulation, analytics, and AI/ML workloads in a single environment,” said Dr. Revati Kulkarni, Technology Head, HPC, AI Group at TCS. “TCS’s HPC A3 solution uses Domino’s capabilities to seamlessly manage heterogeneous compute across complex datasets and helps customers accelerate their transformation journey.”

Domino is also currently available as a managed service from GTS Data Processing as part of its DSready Cloud solution. GTS’ offering —powered by Domino and hosted in Germany— brings together the capabilities its clients need to deliver and manage data science at scale, all within a GDPR-compliant environment that supports Germany’s stringent security standards.

“As we were building DSready Cloud, our data science ecosystem solution, we fell in love with Domino’s comprehensive Enterprise MLOps and security capabilities,” said Lutz Kirchner, Founder of GTS Data Processing. “We also knew Domino’s collaboration with NVIDIA would provide the best experience for self-service access to the compute resources required for leading-edge machine learning and deep learning workloads.”

Domino Platform Validation Underway for NVIDIA AI Enterprise

Validation is underway for the Domino platform on NVIDIA AI Enterprise, an end-to-end, cloud-native suite of AI and data analytics software optimized for the NVIDIA EGX™ platform, running on mainstream NVIDIA-Certified Systems™ from OEM hardware providers and VMware vSphere.

NVIDIA-Certified Systems from partners like Dell Technologies combine the power of a wide range of NVIDIA GPUs with Domino’s Enterprise MLOps platform, providing enterprises a broad selection of high-performance options for scale-out, multi-node GPU-accelerated machine learning. NVIDIA AI Enterprise, running on NVIDIA Certified Systems and VMware vSphere, enables companies to virtualize AI workloads on mainstream servers. Soon, enterprises will be able to more easily and cost-effectively use GPU-based training using the Domino Enterprise MLOps Platform.

“The spectrum of AI in development today spans the work of advanced research organizations that need the world’s most powerful infrastructure, to enterprises exploring how to deploy AI on their mainstream servers,” said Manuvir Das, head of Enterprise Computing at NVIDIA. “Domino Data Lab’s support for NVIDIA DGX systems and its work to validate the NVIDIA AI Enterprise software suite provide a broad range of options for enterprises at every stage of their journey to access powerful NVIDIA AI solutions.”

Domino at GTC

Domino is a diamond sponsor of NVIDIA GTC, which runs today through Thursday, November 11. Learn more by attending Domino Data Lab’s sessions at GTC, which include industry-specific enterprise topics ranging from natural language processing for financial services use cases to selecting the right framework for GPU-accelerated data science.

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