Artifact

Name: Artifact API
Brand: Artifact
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferenceoauth218 EndpointsREST

For Agents

Register, retrieve, and ingest machine learning artifacts (models, weights, run outputs) inside Azure Machine Learning workspaces from an AI agent.

Connect the Artifact to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Artifact, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Install Jentic One Beta (Self-Hosted)

Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with Artifact API.

Retrieve artifact metadata individually or in batches

Ingest artifact content via Azure Blob storage SAS URLs

Download artifact content streams to local or remote consumers

GET STARTED

Start building with Artifact API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for Artifact, keeping it validated and agent-ready.

Artifact is the Azure Machine Learning Services API for storing and retrieving the file-based artifacts that ML workflows produce — model files, trained weights, evaluation outputs, and run inputs. It supports per-workspace artifact registration, batch metadata operations, container ingestion via SAS URLs, and content download. Use this API to wire artifact storage into an ML training, evaluation, or deployment pipeline running on Azure ML.

Use Cases

Patterns agents use Artifact API for, with concrete tasks.

★ Training Run Artifact Capture

ML pipelines running on Azure ML produce checkpoints, evaluation reports, and final model files that must be registered against the run for later retrieval. The Artifact API exposes batch register and metadata endpoints to record all outputs of a run in one call, then retrieve the artifact list when promoting the run to a model registry entry.

Generate a containersas URI, upload run outputs to blob storage, then call the batch register endpoint to record artifact metadata against the workspace.

Cross-Pipeline Artifact Retrieval

Downstream evaluation and deployment pipelines need to fetch the model files produced by an upstream training pipeline. Listing artifacts under the producing run's origin and container, then retrieving content by path, lets a deployment script pull exactly the model file it expects without coupling to internal blob storage paths.

List artifacts under the upstream run's origin and container, find the model.bin entry, then GET its content stream and pipe to local disk.

Bulk Artifact Migration

Teams migrating from one Azure ML workspace to another (region change, subscription move) need to copy artifact metadata efficiently. The batch metadata endpoints let a migration script read all artifact records under a workspace origin in chunks, then re-register them in the target workspace with updated container references.

Read batch metadata from the source workspace's artifacts/batch/metadata endpoint, then register the same set against the target workspace using its register endpoint.

Agent-Driven Artifact Lookup via Jentic

An ML ops AI agent triaging a deployment failure can query Jentic to retrieve the exact artifact metadata for the failing model, without holding the Azure ML workspace credentials. Jentic returns the schema for the metadata endpoint and the agent executes against the workspace, parsing the response to identify whether the deployment is missing a required artifact.

Search Jentic for 'get Azure ML artifact metadata', execute against the workspace, origin, and container, then return the artifact list to the agent for inspection.

Key Endpoints

18 endpoints — artifact is the azure machine learning services api for storing and retrieving the file-based artifacts that ml workflows produce — model files, trained weights, evaluation outputs, and run inputs.

METHOD

PATH

DESCRIPTION

POST

/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/register

POST

/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/batch/metadata

GET

/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/metadata

List artifact metadata under the workspace

GET

/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/{origin}/{container}

List artifacts under a specific origin and container

POST

/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/storageuri/batch/metadata

Get storage URIs for batch artifact metadata

POST

/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/register

POST

/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/batch/metadata

GET

/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/metadata

List artifact metadata under the workspace

GET

/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/{origin}/{container}

List artifacts under a specific origin and container

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Azure AD bearer tokens scoped to the Azure ML workspace are vaulted in Jentic. Agents receive a scoped session at call time and never hold the AAD client secret or workspace key.

Intent-based discovery

Agents search by intent ('register an Azure ML artifact') and Jentic returns the matching operation with its workspace, origin, and container parameter schema, removing the need to navigate the artifact path hierarchy by hand.

Time to first call

Direct integration: 1-2 days for AAD wiring, workspace permission setup, and batch endpoint ergonomics. Through Jentic: under 30 minutes — search, load, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

ML Team Account Management Client

Manage Azure ML team accounts and workspaces that contain these artifacts

Use ML Team Account Management when the agent needs to provision the workspace this artifact API operates against.

Complementary

Azure ML Web Services Management Client

Deploy registered models as web services after artifact capture

Use Azure ML Web Services after artifacts are registered to expose a model as an HTTP endpoint.

Complementary

Azure ML Commitment Plans Management Client

Manage commitment plans that govern Azure ML resource billing

Pair when the agent needs to align artifact storage with a workspace's billing commitment plan.

FAQs

Specific to using Artifact API through Jentic.

What authentication does the Azure ML Artifact API use?

Azure Active Directory OAuth 2.0 bearer tokens. The token must be authorized for the target Azure ML workspace. Jentic vaults the AAD token and issues a scoped session at call time, so the agent never holds the underlying client secret.

Can I register an artifact from a training run with this API?

Yes. POST to /artifacts/register under the workspace path with the artifact origin, container, and path in the request body. For multiple artifacts, use /artifacts/batch/metadata to register a list in a single call, which is the typical pattern for end-of-run capture.

What are the rate limits for the Azure ML Artifact API?

Standard Azure ML data-plane throttling applies. Limits are not exposed in the spec; in practice the batch metadata endpoints handle hundreds of artifacts per call efficiently, while individual register and content endpoints are subject to per-workspace concurrency caps. Use batch endpoints when registering more than a handful of artifacts.

How do I download an artifact's content through Jentic?

Search Jentic for 'download Azure ML artifact content'. Jentic returns the schema for GET /artifacts/{origin}/{container}/{path} (content stream); execute it with the workspace, origin, and container path. The response is the binary content of the artifact file.

Is the Azure ML Artifact API free?

Calls themselves are not separately billed. Storage of artifacts uses the workspace's associated Azure Storage account, which is billed by GB-month and transaction count under standard Blob storage pricing.

How do I ingest a whole container of artifacts at once?

POST to /artifacts/{origin}/{container}/batch/ingest/containersas to receive a write-scoped SAS URI for the target container, upload the files using a standard Blob client, then register the metadata via /artifacts/batch/metadata. This pattern is the most efficient for end-of-run uploads.