# Connect Training and Inference Services Generated: 2026-11-03T08:09:24.346Z Source build: local Canonical docs: https://teammately.ai/docs --- id: integrations.training-inference title: Connect Training and Inference Services summary: Choose provider-specific credentials or identity, validate the connection and configure training or inference separately. kind: reference product_area: data_integrations status: stable updated: 2026-10-31 canonical: /docs/integrations/training-and-inference --- # Connect Training and Inference Services ## Definition Open **Admin → Integrations**, select the provider and configure the connection for the intended account/resource. Use the resulting connection in **Weights training** or the consuming inference workflow. Connection validation is separate from permission, capacity and compatibility for a particular job. ## Provider prerequisites | Provider | Connection inputs | Separate job requirements | | --- | --- | --- | | Azure OpenAI | Resource name, region, resource-origin endpoint and matching key | Eligible model, quota, fine-tuning and deployment rights | | Microsoft Foundry | Resource name, region, resource-origin endpoint and matching key | Eligible model and training/deployment permission | | Together | Key for the intended project | Model eligibility and job capacity | | Fireworks | Account ID and matching key | Selected service/model eligibility and capacity | | Baseten | Key permitted to list training projects | Training project, trainer image/command and data access | | Amazon Bedrock | AWS account, region and integration role ARN | Customization model, service role and job storage access | | Amazon SageMaker AI | AWS account, region and integration role ARN | Training execution role, compute and input/output storage | | Vertex AI | Project ID, region and connection service-account email | Scoped impersonation and job execution identity/resource access | For Azure OpenAI use the resource origin `https://.openai.azure.com`; for Foundry use `https://.services.ai.azure.com`. A project endpoint, deployment URL or API path is a different boundary. Match endpoint and key to the same resource. ## Configure and verify 1. Obtain the intended account/resource identifiers and credential or role details from its administrator. 2. For key-based providers, enter values only in the owning connection flow and use **Verify and save**. Rotate using the existing connection's credential-replacement control. 3. For AWS role connections, obtain the exact trusted backend AWS principal from your Teammately operator and require the displayed workspace external ID in role trust. An external ID is not a principal. Do not substitute a long-lived access key. 4. For Vertex, obtain the exact backend impersonating principal from the operator, and authorize it only for the selected connection service account and resources. Keep connection identity and training execution identity distinct. 5. Select the saved connection, compatible model/method and job-specific resources in Weights training. A Baseten connection check lists accessible training projects. AWS and Vertex checks do not establish every training/storage permission. A successful connection never certifies quota, job creation or training quality. ## Other inference endpoints Choose **Other inference endpoint** for a public HTTPS API base, including an API prefix such as `/v1`. The connection supports Bearer token or Baseten Api-Key authentication. Local/private/IP endpoints and URLs with embedded credentials, ports, queries or fragments are rejected by this connection flow. Verification checks model-list access. It does not test invocation or chat-completion compatibility and does not create a training connection. Check invocation in the consuming workflow against the actual deployment. ## Fields, states, or lifecycle rules Keep connection state, successful probe, job admission, job completion and callable inference deployment separate. Provider training does not automatically allocate serving resources. Inspect the actual job result and separately authorized evaluation before interpreting a candidate's quality. When a connection fails, check matching account/region/endpoint and credential or role trust first. When a job fails after connection validation, inspect its execution role, selected model, storage and quota. Correct the owning provider connection; retain its intended identity/resource boundary. ## Related task pages {% related-card-grid %} - [Export, Train, and Evaluate](/docs/weights-training/exports-and-training) - [Weights training](/docs/weights-training) - [Workspace Integrations](/docs/admin-console/integrations) {% /related-card-grid %} --- id: admin-console.integrations title: Workspace Integrations summary: Connect messaging, repository, trace-store, and training services through their own authorization and operating controls. kind: reference product_area: admin_console status: stable updated: 2026-10-30 canonical: /docs/admin-console/integrations --- # Workspace Integrations ## Definition Open **Admin → Integrations** to connect workspace services. Each connection supplies a specific workflow rather than a general permission to read or write every external resource. ## Fields, states, or lifecycle rules - Messaging connections support their configured notification and test operations. - Repository connections discover repositories accessible to the authorizing account. - Trace-store connections supply their supported capture/import workflow. - Training connections are selected separately by a Benchmark preparation. - Connection state, account authorization and a successful downstream operation remain distinct. ## Messaging Slack and Microsoft Teams have provider-specific connection controls. For Teams, installation and a personal-message test are distinct operations. Workspace settings alone do not confirm installation or that automatic notifications are enabled. Read provider errors as operational outcomes rather than treating a visible integration card as proof of delivery. ## Repositories and trace stores GitHub.com, GitLab.com and Bitbucket Cloud connections discover the repositories allowed by the account and installation. Bind the intended repository through Reference Materials and inspect its indexed content. LangSmith and Langfuse have their own connection surfaces. Keep original record identity and scope when importing material; connected access does not approve the captured behavior. ## Training and inference Weights training selects an active service connection, supported model and compatible method. Connection checks do not certify quota, create-job permission, storage access or training quality. Some evaluation paths require an existing callable inference deployment. A training job does not automatically allocate serving resources for every service. The deployment-to-output association can be user-reported where the adapter identifies it that way. ## Credentials and resource boundaries For setup steps and provider-specific recovery, use the [integration guides](/docs/integrations#connection-setup-guides). Each guide distinguishes workspace connection, Project selection and a successful downstream operation. Use the provider's owning connection flow and approved secret mechanism. Keep access scoped to the authorized account, identity and resource. Existing Vertex and Baseten training adapters do not authorize Google platform storage, ambient developer credentials or a provider fallback. A failed service operation should remain actionable in its owning workflow rather than being hidden by a substitute local service. ## Related task pages {% related-card-grid title="Related task pages" %} - [Reference Materials](/docs/agent-setup/reference-materials) - [Export, Train, and Evaluate](/docs/weights-training/exports-and-training) - [Fieldloop](/docs/fieldloop) - [Admin Console](/docs/admin-console) {% /related-card-grid %} --- id: weights-training.exports-training title: Export, Train, and Evaluate summary: Prepare an exact training package, launch an authorized provider job, and inspect a separately authorized held-out evaluation. kind: task product_area: weights_training status: stable updated: 2026-10-30 canonical: /docs/weights-training/exports-and-training --- # Export, Train, and Evaluate Use Overview to prepare a dataset download and Results to configure connected training and evaluation. Downloading a package does not submit a training job. ## Prerequisites - A reviewed dataset with the intended splits. - An export profile compatible with its method. - An active provider connection for connected training. - An available supported model and explicit authorization to launch. ## Prepare the package Choose **Prepare download** in Overview. Inspect the pinned dataset, profile, counts and exclusions before creating the package. A ready package provides the canonical files and manifest for that exact dataset state. Canonical demonstration and preference exports preserve their method-specific targets. The package can include train, validation and test files where those splits have members, plus its manifest and supporting instructions/materials. A provider-specific conversion is a separate compatibility boundary. ## Train deliberately In Results choose the provider connection, base model and supported settings. Saving configuration alone does not upload data or start a job. Launch explicitly and follow the provider job identity and state. Provider profiles govern supported text, materials, tools, trajectories or preference formats. An unsupported feature is rejected rather than silently removed. A successful connection probe does not establish create permission, quota or successful training. The training transfer uses train and validation as supported. Test content remains held out. Do not upload benchmark Cases or protected rubric/construction context as learner data. ## Evaluate separately Authorize evaluation through the supported inference connection or provider path. Training does not automatically provision an inference deployment for every service. Where the deployment association is user-reported, keep that distinction visible. Inspect actual responses, rubric outcomes, completeness and the resulting model identity. A job that completed without a confirmed model output is not a verified trained candidate. ## Object and state changes Package creation captures the selected dataset. Launch records an authorized training operation and provider job. Cancellation and evaluation have separate operations. Existing packages and accepted results remain attributable. ## Success criteria - Package, dataset and provider job identities agree. - Held-out data stays out of training. - The resulting output is identified before evaluation. - Evaluation uses actual behavior rather than job status. ## Common failure modes - Assuming Save starts training. - Dropping unsupported fields to fit a provider. - Treating training completion as correctness. - Claiming inference resources were provisioned merely because training succeeded. ## Related reference pages {% related-card-grid title="Related reference pages" %} - [Weights Training](/docs/weights-training) - [Training Datasets and Coverage](/docs/weights-training/datasets) - [Workspace Integrations](/docs/admin-console/integrations) - [Inspect Evaluation Results](/docs/benchmark-evaluations/inspect-results) {% /related-card-grid %} ## Related troubleshooting pages {% related-card-grid title="Related troubleshooting pages" %} - [Missing Outputs](/docs/troubleshooting/missing-outputs) - [Benchmark run troubleshooting](/docs/troubleshooting/benchmark-runs) {% /related-card-grid %}