Partnership delivers a unified AI platform with MLOps

April 19, 2021 // By Jean-Pierre Joosting
Partnership delivers a unified AI platform with MLOps
Bundling the AI operating system with Lenovo servers creates an end to end AI system for scalable MLOps and AI training.

A leading AI operating system for machine learning,, along with Lenovo Infrastructure Solutions Group, have released an end to end AI platform built to enable enterprise AI. An MLOps control center, bundled with Lenovo ThinkSystem AI-Ready servers creates a managed, coordinated and easy-to-use system targeted at machine learning (ML) infrastructure. The integrated platform delivers a proven enterprise-grade ML lifecycle platform that increases data science productivity, accelerates AI workflows, and improves accessibility and utilization of AI infrastructure.

Despite extraordinary advancements in the field, Machine Learning and Deep Learning have seen slow adoption in the enterprise. It's reported that nearly 80% of enterprises fail to scale AI deployments across the organization. Data scientists are forced to spend more than half of their time on non-data science tasks and managing disconnected infrastructures. Deploying and maintaining ML systems at scale demands a unified MLOps platform to operationalize the full ML lifecycle from research to production. To achieve this, and Lenovo AI-ready servers deliver a unified hardware and software platform for Machine Learning (ML) and Deep Learning (DL) MLOps.

The platform, running on Lenovo ThinkSystem AI-Ready servers provides data scientists with a unified environment to build, deploy and manage ML and DL workloads, develop and innovate new models, and deploy and monitor models on top of the Lenovo AI purpose built systems. The OS supports data scientists at every stage of the AI lifecycle, delivering solutions to enhance research through to production. The collaborative platform between and Lenovo also provides a single place for IT engineers, DevOps engineers, ML engineers, data scientists and researchers to collaborate, share and achieve AI-driven results. With and Lenovo, data scientists can build dynamic end-to-end ML/DL systems on heterogeneous compute by running different tasks on maximized CPU and GPUs.

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