Over 100 NVIDIA MLOps and AI Platform Partners Help Enterprises Move AI to Production

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Building AI applications is hard. Using them across your business can be even more difficult.

According to a recent IDC study, less than one-third of companies that have started to adopt AI are in production.

Many companies often discover the complexity of operationalizing AI just before launching an application. Deployment efforts are often bogged down or forgotten because problems discovered too late can appear insurmountable.

More than 100 machine learning operations (MLOps) software providers are working with NVIDIA to help enterprises take the final stages of AI adoption. These MLOps pioneers offer a wide range of solutions to help companies optimize AI workflows for both existing operational pipelines and pipelines built from the ground up.

Many NVIDIA MLOps and AI platform ecosystem partners, including Canonical, ClearML, Dataiku, Domino Data Lab, Run:ai, Weights & Biases, and DGX-Ready software partners, have integrated NVIDIA acceleration infrastructure and software to I’m building a solution that meets the following criteria: The needs of enterprises operating AI.

Other partners around the world, including NVIDIA cloud service provider partners Amazon Web Services, Google Cloud, Azure, Oracle Cloud, and Alibaba Cloud, also offer MLOps solutions to streamline AI deployments.

NVIDIA’s leading MLOps software partners are validated and certified for use in the NVIDIA AI enterprise software suite, which provides an end-to-end platform for creating and accelerating production AI. Combining tools from NVIDIA’s MLOps partners with NVIDIA AI Enterprise enables enterprises to successfully develop and deploy AI.

Enterprises can put AI to work with the help of these and other NVIDIA MLOps and AI platform partners.

  • Canonical: It aims to accelerate AI deployment at scale while making open source available for AI development. Canonical announced that Charmed Kubeflow has been certified as part of the DGX-Ready software program for both single-node and multi-node deployments on his NVIDIA DGX systems. Designed to automate machine learning workflows, Charmed Kubeflow creates a reliable application layer that allows you to move your models into production.
  • ClearML: An integrated open source platform for continuous machine learning, from experiment management and orchestration to performance improvement and ML production, trusted by teams in 1,300 companies worldwide. ClearML enables enterprises to coordinate and schedule jobs on a personalized computing fabric. Whether on-premises or in the cloud, businesses can increase visibility into infrastructure usage while reducing compute, hardware and resource spending to optimize cost and performance. Now certified to run on NVIDIA AI Enterprise, ClearML’s MLOps platform is more efficient across workflows and improves optimization for GPU power.
  • dirty: As a platform for everyday AI, Dataiku enables data and domain experts to collaborate and incorporate AI into their daily work. Dataiku is now certified as part of the NVIDIA DGX-Ready software program, allowing businesses to confidently use Dataiku’s MLOps capabilities alongside NVIDIA DGX AI supercomputers.
  • domino data lab: Provides a single pane of glass that enables the world’s most sophisticated enterprises to run their data science and machine learning workloads on any compute cluster, on any cloud or on-premises in any region. Domino Cloud, a new platform-as-a-service for fully managed MLOps, is now fast and easy for data science at scale. Certified to run on NVIDIA AI Enterprise last year, the Domino Data Labs platform reduces deployment risk and ensures reliable, high-performance integration with NVIDIA AI.
  • Iguazio: We provide a platform to automate, accelerate, and scale MLOps, transforming AI projects into real-world business outcomes. Iguazio’s global client uses its “ML Factory”. This allows you to automate the development, deployment, and management of AI applications in real-world business situations while continuously deploying new AI services in a repeatable, scalable, and reproducible manner. The platform works across a variety of use cases and on-premises, multi-cloud, and hybrid cloud environments. Iguazio is a DGX-Ready Certified Partner and recently NVIDIA AI Accelerated Partner.
  • Ran: Ai: Acts as a foundational layer within an enterprise’s MLOps and AI infrastructure stack through its AI computing platform, Atlas. The platform’s automated resource management capabilities enable organizations to better coordinate resources across the various MLOps platforms and tools running on the Run:ai Atlas. Certified to deliver the NVIDIA AI enterprise, Run:ai also fully integrates the NVIDIA Triton inference server, maximizing GPU utilization and value in AI-powered environments.
  • Weights and Bias (W&B): Helps machine learning teams build better models faster. With just a few lines of code, practitioners can instantly debug, compare, and reproduce models while collaborating with teammates. W&B is trusted by her over 500,000 machine learning practitioners at leading companies and research institutions around the world. Now validated to offer NVIDIA AI Enterprise, W&B is looking to accelerate deep learning workloads across computer vision, natural language processing and generative AI.

NVIDIA cloud service provider partners integrate MLOps into platforms that offer NVIDIA accelerated computing and software for data processing, wrangling, training, and inference.

  • Amazon Web Services: Amazon SageMaker for MLOps helps developers automate and standardize processes across the machine learning lifecycle using NVIDIA Accelerated Computing. It increases productivity by training, testing, troubleshooting, deploying, and managing ML models.
  • Google cloud: Vertex AI is a fully managed ML platform that brings together a wide range of purpose-built capabilities to help accelerate ML deployments. Vertex AI’s end-to-end MLOps capabilities make it easy to train, orchestrate, deploy, and manage ML at scale using NVIDIA GPUs optimized for a variety of AI workloads. Vertex AI also supports state-of-the-art solutions such as the NVIDIA Merlin framework to maximize performance and simplify large-scale model deployment. Google Cloud and NVIDIA have teamed up to add Triton Inference Server as the backend for Vertex AI Prediction, Google Cloud’s fully managed model serving platform.
  • Azure: The Azure Machine Learning cloud platform, accelerated by NVIDIA, unifies ML model development and operations (DevOps). It aims to apply DevOps principles and practices (continuous integration, delivery, deployment, etc.) to machine learning processes to accelerate experimentation, development, and deployment of Azure machine learning models into production. . It provides quality assurance through built-in responsible AI tools, enabling ML professionals to develop fair, accountable, and responsible models.
  • Oracle cloud: Oracle Cloud Infrastructure (OCI) AI Services is a collection of services with pre-built machine learning models that make it easy for developers to apply NVIDIA-accelerated AI to their applications and business operations. Teams within your organization can reuse models, datasets, and data labels across services. OCI AI Services enable developers to easily add machine learning to their applications without slowing application development.
  • Alibaba Cloud: Alibaba Cloud Machine Learning Platform for AI provides an all-in-one machine learning service with low user technical skill requirements and high performance. Accelerated by NVIDIA, the Alibaba Cloud platform enables enterprises to quickly establish and deploy machine learning experiments to achieve their business goals.

Learn about NVIDIA MLOps partners and their work at NVIDIA GTC, the global conference for the age of AI and the Metaverse, taking place online through Thursday, March 23rd.

Watch a replay of NVIDIA founder and CEO Jensen Huang’s GTC keynote.

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