Question: Can you recommend a solution that allows me to build, deploy, and manage AI workloads across multiple clouds and on-premise environments?

If you need a full-stack solution to build, deploy and manage AI workloads on multiple clouds and on-premises systems, Anyscale is a top contender. The platform lets you build and scale AI applications with the best performance and efficiency. It supports a broad variety of AI models and has heterogeneous node control, GPU and CPU partitioning for better resource allocation. Anyscale also offers native integrations with popular IDEs and smart instance management, making it a good choice for enterprise use cases.

Another powerful contender is Oracle Cloud Infrastructure (OCI). OCI is a wide-ranging collection of cloud services that can be deployed in public clouds, on-premises or in hybrid environments. It offers multicloud, public cloud, hybrid cloud and dedicated cloud services, including AI and machine learning, big data and compute. With competitive pricing and strong security options, OCI is a good fit for large enterprises that need flexibility and scale.

For those who prefer an open-source option, dstack automates infrastructure provisioning for developing and deploying AI models on a variety of cloud providers and data centers. It supports a variety of cloud providers and on-prem servers, making it easier to set up and deploy AI workloads. dstack's features, such as dev environments and cost-effective GPU usage, can help you cut costs and focus on AI development.

Last, IBM Cloud is a powerful option for highly regulated industries, with a secure, resilient and high-performance foundation for AI applications. It includes options for fast deployment of AI models, strong security and compliance, and easy scaling and governance. IBM Cloud also offers a host of free products and promotional offers, making it a good option for companies that want to build and manage AI workloads.