Offers Early Access to Its Managed AI Platform, Metacloud Metacloud, a new managed service that gives AI developers full flexibility to run AI/ML workloads on a mix of infrastructure and hardware choices, even within the same AI/ML workflow or pipeline, was announced last week by Built by data scientists, this operating system for artificial intelligence and machine learning is now available for exclusive early access on request.

Available platform integrations include Intel, AWS, Azure, GCP, Dell, Redhat, VMWare, Seagate, and more.

Many AI initiatives are stuck because existing IT infrastructure (cloud or on-premise) is unable to satisfy the rising needs of AI workloads, stated – a company that was acquired by Intel in 2020. AI developers would frequently be bound to a single infrastructure design, which may limit their ability to experiment with new and innovative ML/AI infrastructure possibilities.

Data scientists must re-instrument an entirely new stack in order to experiment with different surroundings, which might take months to set up. AI developers want the option to rapidly select the best-of-breed compute and cloud solution for each task, depending on each architecture’s cost/performance trade-offs, without the need to commit to a long-term commercial relationship.

“AI has yet to meet its ultimate potential by overcoming all the operational complexities. The future of machine learning is dependent on the ability to deliver models seamlessly using the best infrastructure available,” said Yochay Ettun, CEO and co-founder of “ Metacloud is built to give flexibility and choice to AI developers to enable successful development of AI instead of limiting them, so enterprises can realize the full benefits of machine learning sooner.”

AI/ML Workflows

Photo Yochay Ettun, CEO and co-founder of
“ Metacloud is built to give flexibility and choice to AI developers to enable successful development of AI instead of limiting them,” said Yochay Ettun, CEO and co-founder of

With the early release of Metacloud, AI developers now have the entire flexibility and choice to execute any AI architecture for any AI workload on demand. Together with the end-to-end operating system for machine learning, AI developers can now manage data, develop, train, and deploy models on any infrastructure instantly.

By giving a developer-friendly gateway to set-up and launch AI/ML workflows utilizing any hardware or storage service accessible from a partner menu, Metacloud would deliver a new flexible interface for executing AI workloads instantly: BYOC (Bring Your Own Compute) and BYOS (Bring Your Own Storage).

Because Metacloud is built on cloud native technologies like containers and Kubernetes, it works with any AI infrastructure provider. Developers just need to establish an account, choose the AI/ML infrastructure they want to use to run their project (any public cloud, on-premises, co-located, dev cloud, pre-release hardware, and more), and start the workload.

“Two years ago, Wargaming was stalled with an aging technology that wasn’t scaling to its growing needs for ML&AI.  We transformed our ML pipeline with that provided us with the scalability and flexibility to develop and deploy algorithms in production in a broader and more efficient manner,” said Jonathan Crow, Senior Director of Data Science at Wargaming. “Having fully integrated into our ML stack we are continuing to explore and expand its capabilities to benefit our business. Recently, we started deploying on the cloud (in addition to on-prem) to provide our users with quicker access to needed resources to do their jobs. We’re excited for Metacloud as a new way to get immediate access to any AI/ML infrastructure in the market that is best suited for the work in just one click.”

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