Build AI Cloud with InfraCloud AI Platform

With advances in Gen AI, data privacy and security are more important than ever. With 137+ countries enacting some form of data protection and sovereignty laws, build AI cloud with data residency policies in place for data protection and privacy. With InfraCloud Sovereign AI Cloud, you can achieve digital sovereignty without stressing about all the operational complexity.

• -> InfraCloud AI platform enables building a Sovereign AI Cloud in a colo facility or your data center, so you can control where you locate your data and computing infrastructure.
• -> Our platform follows the three aspects of sovereignty right from the start: data sovereignty, operational sovereignty, and software sovereignty.
• -> Build AI infrastructure that ensures compliance with local regulations (like GDPR and Schrems II) and fulfills the transparency obligations.
• -> Platform can burst into the public cloud for specific scale needs and communication with public AI systems while preserving the data residency policies.

With InfraCloud AI MLOps Platform, data scientists and engineers can build, train & deploy models and run AI and MLOps experiments without spending energy and resources managing GPU cloud infrastructure. Manage multiple cloud resources, data sources, server requests, system performance, logs, policies, etc, and administer all the management and business functionality through a single pane of glass with the InfraCloud AI Control plane.

• -> Test various experiments for AI business use cases without worrying about setting up the MLOps pipeline while using the various foundation models from the open source world to author notebooks.
• -> Connect to data sources and clean data before using with models/notebooks to maintain output accuracy and relevancy. Build models and train them on a distributed cluster for faster training & tracking of the experiments.
• -> Deploy models to a choice of inference servers based on your needs, which are fine-tuned with the underlying infrastructure. Track requests to inference servers and optimize & debug based on monitoring & log data of the inference servers to keep MLOps in a healthy state.
• -> Handle & operate on-premise and on cloud clusters and workloads along with underlying infrastructure from one user-friendly dashboard without any learning curve.
• -> Monitor your system’s performance by tracking GPU, memory, and storage usage across your entire AI infrastructure in real-time and overview access control and audit logs of all operations on the platform to discover the unwanted waste of resources and downtime quickly.