The outcome
Learn the interaction loop using a small task with a clear outcome.
Baseten is a managed platform for packaging, deploying, scaling, and operating AI models. Turning open or custom models into versioned autoscaling APIs with development deployments, stable environments, observability, and controlled promotion. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.
Before you begin
Set the boundary before the tool starts.
Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.
AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.
Step by step
A workflow you can repeat.
- 01
Define the model, license, endpoint schema, secrets, dependencies, GPU requirement, and acceptance tests.
- 02
Create a reproducible Truss configuration and push an isolated development deployment.
- 03
Inspect build and load logs, then test quality, errors, concurrency, latency, and resource use.
- 04
Publish and promote only after staging evaluation, retaining the previous deployment for immediate rollback.
Working standard
What good use looks like.
- State the outcome before the background.
- Provide the real source material.
- Review the result before expanding the task.
GPU deployments can create ongoing cost and production logs or traces may contain sensitive data. Use secret references, environment separation, bounded autoscaling, output validation, access controls, and reversible promotions.
Official references
Check the current product documentation.
Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.