End-to-End Machine Learning Infrastructure 
EMA Top 3 award-winning products enable data scientists, data engineers, DevOps teams, application operators, and developers to rapidly provision, manage, scale, move, and terminate machine learning models in a reliable, secure, and cost-effective manner.
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The ability of an MLOps infrastructure platform to share critical resources like GPU, TPU, CPU, RAM, and storage between teams and projects enables organizations to squeeze the most value out of these resources, and therefore constitutes a crucial requirement for EMA Top 3 products.

To ensure optimal compliance, performance, and security, EMA Top 3 products need to support the policy-driven placement and management of data science workloads across data center infrastructure, multi-cloud, and edge locations.

EMA Top 3 award winners in the “MLOps: End-to-End Machine Learning Infrastructure” category need to enable current staff to use their existing development and operations tools and infrastructure in the most impactful manner. This enables each persona to focus on its individual set of key tasks without having to learn new tools, languages, or understanding the inner workings of the platform itself. 

Why Red Hat OpenShift Received the EMA Top 3 Award
The Red Hat OpenShift open hybrid cloud platform enables organizations to rapidly spin up new machine learning stacks and move machine learning models through the MLOps pipeline for policy-driven (and therefore consistent) deployment on any Kubernetes cluster.

The Open Data Hub Operator turns OpenShift into a Kubernetes-based machine learning as a service platform, enabling developers to request complete machine learning stacks consisting of a customizable set of popular open-source components based on the Kubeflow machine learning toolkit for Kubernetes. Ceph provides the object storage required for the infrastructure-independent data back end of Open Data Hub. Red Hat OpenShift Data Science offers a turnkey Open Data Hub-based managed data science environment as part of the Red Hat OpenShift Dedicated or Red Hat OpenShift Service on AWS for instant use. 

Website: Red Hat OpenShift

Business Impact

• Machine learning as a service directly enhances the productivity of data scientists and data engineers.
• One platform for MLOps and DevOps accelerates developer adoption of machine learning models.
• Operational consistency for applications and machine learning models drives down OPEX. 
• The combination of open-source and commercial machine learning tools and libraries ensures flexibility.
• Unified management of apps and learning models across data center, public cloud, and edge is the foundation for continuous compliance.