In the news: Zeblok Featured by Intel at Mobile World Congress 2022

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TM

Ai-MicroCloud

 Ubiquitous    Full Stack       Full Lifecycle     Run Anywhere

Zeblok  Ai-MicroCloud™️ enables Ai PaaS & Ai SaaS. 

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Zeblok’s Ai-MicroCloud™ solves the problem of scaling at the Edge, making it easy to deploy Ai inferences to thousands of Edge locations, with end-to-end lifecycle management. Zeblok also addresses critical enterprise Ai gaps, including optimization of Ai/ML models for heterogeneous chipsets.

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Innovative digital Ai assets drive Digital Transformation 3.0. As the API economy changed the landscape a decade ago, the Ai-API economy will lower cost per insight.

Enterprise Ai

This year 50% of enterprises plan to spend more on Ai and ML, with 20% saying they will be significantly increasing their budgets. But 84% of the Ai projects fail, and Ai/ML adoption in enterprises is a meager 6%. Enterprises must integrate pragmatic Ai into mission-critical processes, while simultaneously dealing with a multi-cloud strategy. CIOs have to deal with large number of Ai ISVs and curate digital assets for the enterprise customers and partners. They need Ai PaaS and Ai SaaS to accomplish this goal. Furthermore, they must optimize Ai/ML models for heterogeneous architectures.

Edge Ai

By 2035 a trillion edge devices will be operational, requiring millions of Multi-Access Edge Computing (MEC) data centers. While Ai ISVs develop solutions in public cloud environments, there is gap between functional Ai Inference engines in the public cloud and secure deployment of such capabilities at MECs. ML DevOps must extend from public cloud or on-prem data centers into Edge data centers. CIOs must deal with a large number of Ai ISVs and curate digital assets for Edge device consumption. They need Ai PaaS and Ai SaaS to accomplish this goal.

Versatile, Robust Platform

Developer

Data Scientist

Product

ML DevOps

Zeblok's Ai-MicroCloud™ provides a single, cohesive, turnkey, cloud native Ai environment for development, testing, training and deployment of an end-to-end Ai/ML solution to production from Cloud to Edge.

It saves 12-18 months of engineering effort to integrate the multitude of technologies, countless hours of business effort to establish a compatible and collaborative Ai ecosystem, and reduces GTM for ISVs by 8-12 months.

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