Ai-MicroCloud Lifecycle Managment
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Ai-MicroCloud is an end-to-end platform that empowers organizations to design, deploy, secure, operate, and optimize AI solutions at the edge — from the cloud to the farthest satellite nodes.
Our AI Lifecycle Management philosophy includes:
Design: Architect flexible topologies for public cloud, on-prem, and edge (MEC) environments.
Deploy: Scale effortlessly across certified hardware at distributed edge locations.
Secure: Enterprise-grade security with LDAP, RBAC, and seamless policy integration.
Operate: Curate and deploy AI pipelines through Ai-AppStore and serve inferences via Ai-API Engine.
Optimize: Leverage NVIDIA and Intel toolchains to maximize inference performance.
Monitor: Proactive health checks and resource monitoring across the entire AI stack.
Manage: Simplify updates across AI applications, Kubernetes, and platform components.
Support: Connect alerts to third-party ticketing for NOC-level support readiness.
Ai-MicroCloud uniquely combines AI-model operations and Infrastructure-as-Code to create a comprehensive AI software lifecycle management platform.
Securely establish Ai-MicroCloud® with launcher on your choice of hyperscaler, on-premises, or edge.
Aggregate foundational models and significantly reduce model fine-tuning, model serving, and testing times.
Establish guard rails and optimize models for policy- driven deployments
Integrate with enterprise developer toolchains
Monitor model performance.
Advanced modelers can fine-tune and test via SDK right from their laptops.
With Zeblok, IT becomes low-touch and fully automated, enabling multi-disciplinary teams to collaborate and increase Launch reuse, resulting in organizational productivity.
Ai-MicroCloud manages composable components and resources. With Plug-n-Play capabilities, the platform is extensible across any Ai use case and workload from Cloud Service Providers to Enterprise Ai.
With cloud-to-edge support, Ai- MicroCloud® orchestrates the entire, end-to-end ML-Ops process from containers to static & dynamic allocation of computer resources.
Ai-MicroCloud can be provisioned in less than 15 minutes on most Hyperscalar environments, and in less than 30 minutes on Bare Metal. With automation and streamlined workflows, no cloud or infrastructure skills are needed to increase efficiency. control rising cloud spending and enhance security. This allows greater flexibility, and resources can be focused on development and analysis. Now enterprises can be DL & ML Dev Sec Ops platform to Data instead of other way around
Ai-MicroCloud auto scale for HPC fine-tuning workloads saving critical for training experiment pipelines, and Ai-inference workloads.
The platform also scales out secure Ai-APIs and can deploy Ai-inference engines to 1000s of Edge Al locations with minimal manual effort. This not only saves time and resources but stretches the reach of Ai-inference engines anywhere.
