Model Training

Model training in the Admin UI sidebar groups AIOps pages for experiment tracking and infrastructure operations.

Use this section when you need to run and compare training workloads, manage model/dataset registries, or operate shared runtimes and cloud power schedules.

Next: open Experiment Tracking for tasks, models, datasets, and artifacts, or Environments & Operations for shared environments, cloud connections, power schedules, and integrations. For AI Gateway and FinOps pages above this section, see Core Services.

Table 1: OptScale AI Model Training — documentation areas

Documentation	Sidebar pages	Read more
Experiment Tracking	Tasks, Models, Datasets, Artifacts	Experiment Tracking
Environments & Operations	Shared Environments, Cloud Connections, Power Schedules, Integrations	Environments & Operations

Menu section overview

Table 2: OptScale AI Model Training — sidebar menu items

Menu item	Purpose
Tasks	Create, run, and monitor training or pipeline executions.
Models	Register and manage model versions, metadata, and lifecycle stages.
Datasets	Organize and version datasets used by training and evaluation runs.
Artifacts	Store run outputs such as checkpoints, logs, and generated files.
Shared Environments	Manage shared runtime environments used by teams for training and testing.
Cloud Connections	Configure cloud account integrations used by environments and compute resources.
Power Schedules	Define automated start and stop schedules for infrastructure resources.
Integrations	Connect external systems and CI/CD services used in ML workflows.

See also

• Architecture Overview — Platform structure and request flow.
• First Steps — Initial organization and provider setup.
• Interface Overview — Chat workspace for end users.