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Artificial intelligence and machine learning are changing the future while disrupting the present. While it is deceptively easy to launch successful AI pilots, moving toward an organization-wide program to realize the full value potential is fiendishly hard. Using ML Ops to effectively manage and govern the AI lifecycle from experimentation to production is the business’s next competitive frontier
Ritu Jyoti
Program Vice President, Artificial Intelligence Research, Global AI Research Lead, IDCThe platform for Operationalizing Machine Learning
Scale your machine learning development from research to production with an end-to-end solution that gives your data science team all the tools they need in one place

Accelerate Experimentation
Build high-quality models and release them to production faster, with self-serve access to the latest tools and scalable compute.

Deliver Models to Production
Deploy models in one click on industrial-grade, auto-scaling, Kubernetes-based infrastructure.

Monitor and Govern
Complete visibility with Real-time insights and alerts on model performance with enterprise-grade security and Governance.
A Unified UI to manage all your data science in one place.

7x
More cost-effective infrastructureReduction in computing costs through efficient management of data science work loads.
12x
Faster deploymentsFaster and a more reliable way to deploy and improve models in production
85%
Reduction in manual labour costsReduction in manual labour costs resulting from higher productivity of data science teams