Managed AI Environment Discussions for Indian Teams

Define who needs access and how work will run

Clarify the people and systems that must use the environment, along with the access pattern they expect. These are requirements to discuss, not a statement that a particular identity, network, logging, or administration model is included.

• Which teams, service accounts, administrators, and external collaborators need access?
• What identity, approval, audit, and separation-of-duty questions must be evaluated?
• How will notebooks, jobs, model artifacts, and deployment handoffs be operated?

Make storage and framework requirements explicit

Data volume, movement, retention, and recovery expectations shape the environment discussion. Frameworks such as PyTorch, TensorFlow, JAX, CUDA tooling, containers, or MLOps components can be raised as requirements; they are not represented here as confirmed supported software.

• What data enters the environment, where is it currently held, and who may access it?
• What storage capacity, performance, lifecycle, retention, and export requirements apply?
• Which framework versions, package controls, images, registries, and integrations need validation?

Scope an operating model before commercial commitment

Aitonomi AG can connect environment requirements with dedicated GPU capacity and deployment-support discussions. Final scope needs to address data location, residency requirements, eligible use, responsibilities, support process, configuration, commercial terms, and the written agreement.

• List the workload, GPU-memory profile, expected users, and planned evaluation or production phase.
• Separate mandatory controls from preferences so open items can be resolved clearly.
• Agree the implementation responsibilities, review gates, and acceptance evidence to request.