Accelerate Production AI and Machine Learning With MLOps
Machine learning operations (MLOps) is the overarching concept covering the core tools, processes, and best practices for end-to-end machine learning system development and operations in production. The growing infusion of AI into enterprise applications is creating a need for the continuous delivery and automation of AI workloads. Simplify the deployment of AI models in production with NVIDIA’s accelerated computing solutions for MLOps and a partner ecosystem of software products and cloud services.
MLOps can be extended to develop and operationalize generative AI solutions (GenAIOps) to manage the entire lifecycle of gen AI models. Learn more about GenAIOps here.
What Is MLOps?
Machine learning operations, or MLOps, refers to the principles, practices, culture, and tools that enable organizations to develop, deploy, and maintain production machine learning (ML) and AI systems.
Optimize the AI and machine learning pipeline with ease at scale.
Streamline AI Deployment
The NVIDIA DGX™-Ready Software program features enterprise-grade MLOps solutions that accelerate AI workflows and improve the deployment, accessibility, and utilization of AI infrastructure. DGX-Ready Software is tested and certified for use on DGX systems, helping you get the most out of your AI platform investment.
The software layer of the NVIDIA AI platform, NVIDIA AI Enterprise, accelerates data science pipelines and streamlines development and deployment of production AI, including generative AI, computer vision, speech AI, and more. With over 100 frameworks, pretrained models, and development tools, NVIDIA AI Enterprise is designed to accelerate enterprises to the leading edge of AI and deliver enterprise-ready MLOps with enterprise-grade security, reliability, API stability, and support.
Accelerated MLOps infrastructure can be deployed anywhere—from mainstream NVIDIA-Certified Systems™ and DGX systems to the public cloud—making your AI projects portable across today’s increasingly multi- and hybrid-cloud data centers.
Initiate your generative AI projects with NVIDIA Blueprints. Enterprises can build and operationalize custom AI applications—creating data-driven AI flywheels—using Blueprints along with NVIDIA AI and Omniverse libraries, SDKs, and microservices.
Quickly get started with reference applications for generative AI use cases, such as digital humans and multimodal retrieval-augmented generation (RAG).
Blueprints include partner microservices, one or more AI agents, reference code, customization documentation, and a Helm chart for deployment.
See how NVIDIA AI Enterprise supports industry use cases, and jump-start your development with curated examples.
MLOps for Automotive
MLOps for Recommendation Systems
MLOps for Automotive
Automotive use cases federate multimodal data (video, RADAR/LIDAR, geospatial, and telemetry data) and require sophisticated preprocessing and labeling with the ultimate goal of a system that will help human drivers negotiate roads and highways more efficiently and safely.
Unsurprisingly, many of the challenges automotive ML systems face are related to data federation, curation, labeling, and training models to run on edge hardware in a vehicle. However, there are other challenges unique to operating in the physical world and deploying to an often-disconnected device. Data scientists working on ML for autonomous vehicles must simulate the behavior of their models before deploying them, and ML engineers must have a strategy for deploying over-the-air updates and identifying widespread problems or data drift from data in the field.
Recommendation systems are ubiquitous in consumer and enterprise applications alike for retail, media, advertising, and general search use cases, among many others. These systems incorporate multiple models and rule-based components; they also process enormous amounts of data and can have tremendous economic impact.
Because recommendation systems are often deployed in highly dynamic environments, retrieval and scoring models may need to be retrained multiple times per day, and data scientists will often need to figure out how to tailor their performance to maximize business metrics. This is even more complicated because the overall system will depend on the interaction between trained models (for finding relevant suggestions and scoring a filtered list) and business rules (for filtering irrelevant suggestions and ordering the final results).
Data scientists thus need a flexible environment to design and track experiments, test hypotheses, and define metrics to monitor in production. Machine learning engineers need tooling to define, execute, and monitor training pipelines, as well as to monitor the performance of the overall system.
Securiti to Empower Organizations to Build Safe, High-Performance Enterprise AI Systems With NVIDIA NIM Microservices
Securiti announced at Money 20/20 that it has integrated NVIDIA NIM microservices into its Securiti Gencore AI solution. This empowers users in industries such as financial services to more easily and quickly build safe, enterprise-grade generative AI systems, copilots, and AI agents, utilizing proprietary enterprise data safely in diverse data systems and apps.
Dataiku Accelerates Enterprise-Ready AI and Analytics With NVIDIA
With Dataiku and NVIDIA, any team member can leverage AI capabilities and collaborate with others to deliver more projects. Through Dataiku's no-code and code-based data science capabilities, along with powerful AutoML, practitioners of all levels can find the best models and seamlessly support code notebooks, IDEs, and CI/CD tools.
JFrog Collaborates With NVIDIA to Deliver Secure AI Models With NVIDIA NIM
JFrog Artifactory provides a single solution for housing and managing all the artifacts, binaries, packages, files, containers, and components for use throughout software supply chains. The JFrog Platform’s integration with NVIDIA NIM is expected to incorporate containerized AI models as software packages into existing software development workflows.
The boom in AI has seen a rising demand for better AI infrastructure—both in the compute hardware layer and in the AI framework optimizations that make maximum use of accelerated computing.
AI is impacting every industry, from improving customer service to accelerating cancer research. Explore the best practices for developing an efficient MLOps platform.
Take advantage of our comprehensive LLM learning path, covering fundamental to advanced topics and featuring hands-on training developed and delivered by NVIDIA experts. You can opt for the flexibility of self-paced courses or enroll in instructor-led workshops to earn certificates of competency.
Deploying Generative AI in Production With NVIDIA NIM
Unlock the potential of generative AI with NVIDIA NIM. This video dives into how NVIDIA NIM microservices can transform your AI deployment into a production-ready powerhouse.
Jensen Huang shares the story behind NVIDIA's expansion from gaming to deep learning acceleration, leadership lessons that he's learned over the last few decades, and the importance of MLOps.
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