End-to-end MLOps and LLMOpsPowered by Domino & Intel
Build and Operate AI with MLOps and LLMOps on Domino's Enterprise AI Platform powered by Intel CPUs. Accelerate time-to-insight with AI and Deep Learning.
Domino provides Enterprise MLOps capabilities to build, deploy, and monitor models at scale. Any model, any data, any cluster - in data centers, the hybrid/multi-cloud, or at the edge.
With Domino powered by Intel® 4th Gen Xeon® Scalable Processors with Built-In Accelerators, compress the time between AI development and deployment at scale.
Unify AI Workloads Anywhere
Accelerate workload training and inference
Leverage Intel® 4th Gen Xeon® Processors (Sapphire Rapids) - on prem and in the cloud
Simplify AI workload management across architectures
Accelerate AI pipelines and bring models to production faster
Collaborate across data science teams, technologies, and locations
Leverage pre-trained models and container images
Optimize infrastructure for leading price performance in the cloud and on-premises
Achieve better TCO and ROI from central compute and storage resources
Reduce internal support costs for DevOps while consolidating disparate stacks
Provide self-service, governed access to data and compute resources
Monitor models and data across compute clusters while complying with data privacy and sovereignty rules
Track cost and usage back to business units, projects, and teams
Build and Tune Models Quickly
- Build fast with pre-trained models and container images in Habana Model Catalog.
- Collaborate across data science teams, technologies, and locations in Domino's Enterprise AI Platform.
Learn more about LLMs on CPUs
Achieve End-to-End Performance for AI Workloads Powered by oneAPI
Intel® AI Analytics Toolkit (AI Kit)
The AI Kit gives data scientists, AI developers, and researchers familiar Python* tools and frameworks to accelerate end-to-end data science and analytics pipelines on Intel® architecture.
Domino’s growing partner ecosystem helps our customers accelerate the development and delivery of models with key capabilities of infrastructure automation, seamless collaboration, and automated reproducibility. This greatly increases the productivity of data scientists and removes bottlenecks in the data science lifecycle.