Microsoft Azure APIs

237 APIs4,900 Endpoints17 Categories

API Catalog(21 of 237)

Artifact

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.

18 endpoints

Azure Anomaly Finder Client

Jentic publishes the only available OpenAPI specification for Azure Anomaly Finder Client, keeping it validated and agent-ready. The API runs unsupervised anomaly detection on numeric time series data. Two operations cover the common modes: batch detection over an entire series with a model trained on the supplied points, and last-point detection that scores only the most recent value against a model trained on the prior history. It is a building block for incident detection, fraud signals, and capacity-planning alerts.

2 endpoints

Azure Enterprise Knowledge Graph Service

The Azure Enterprise Knowledge Graph Service Resource Provider API manages knowledge-graph services as ARM resources under Microsoft.EnterpriseKnowledgeGraph. It exposes seven endpoints to list services across a subscription or resource group, retrieve a service, and create, update, or delete graph instances. The service itself was a preview offering for building enterprise knowledge graphs at scale; the API surface here is the control plane that provisions the graph backend.

7 endpoints

Azure Machine Learning Datastore Management Client

Jentic publishes the only available OpenAPI specification for Azure Machine Learning Datastore Management Client, keeping it validated and agent-ready. This API manages datastores attached to an Azure Machine Learning workspace — registered references to Azure Blob, Azure File, Azure Data Lake, SQL, and PostgreSQL data sources that training jobs and pipelines mount instead of hard-coding storage credentials. Operators can register a datastore, list every datastore on a workspace, set or read the workspace default, and remove datastores that are no longer used.

8 endpoints

Azure Machine Learning Model Management Service

Jentic publishes the only available OpenAPI specification for Azure Machine Learning Model Management Service, keeping it validated and agent-ready. This API manages the registered models, images, profiles, and deployed inference services on an Azure Machine Learning workspace. Operators can register a model, build a containerised image, profile its resource requirements, deploy it as a real-time service, and retrieve scoring keys or short-lived bearer tokens for callers. The API exposes 23 endpoints under the /modelmanagement/v1.0 prefix and authenticates with Azure AD OAuth 2.0.

23 endpoints

Azure Machine Learning Workspaces

Jentic publishes the only available OpenAPI specification for Azure Machine Learning Workspaces, keeping it validated and agent-ready. This API manages the Microsoft.MachineLearningServices/workspaces resource and the compute targets attached to it. Operators can provision a workspace, list workspaces across subscriptions or resource groups, attach compute clusters or compute instances, list available VM sizes, manage region quotas, and read or resync the workspace's storage and registry keys. All requests authenticate with Azure AD OAuth 2.0 against Azure Resource Manager.

20 endpoints

Azure ML Commitment Plans Management Client

Jentic publishes the only available OpenAPI specification for Azure ML Commitment Plans Management Client, keeping it validated and agent-ready. The API manages Azure Machine Learning commitment plans and their child commitment association resources under the Microsoft.MachineLearning provider. It supports full CRUD on commitment plans, listing and reading commitment associations, moving associations between plans, and retrieving plan usage history so finance and platform teams can track committed ML capacity.

12 endpoints

Azure ML Web Services Management Client

Jentic publishes the only available OpenAPI specification for Azure ML Web Services Management Client, keeping it validated and agent-ready. The API manages Azure Machine Learning web services published from ML Studio (classic), supporting create-or-update, get, patch, delete, region-blob creation, and key retrieval under the Microsoft.MachineLearning provider. It is the control surface for deploying inference endpoints, rotating their access keys, and listing every web service in a resource group or subscription.

9 endpoints

BatchAI

Jentic publishes the only available OpenAPI specification for BatchAI, keeping it validated and agent-ready. The Azure BatchAI Management API provisions GPU-enabled clusters, file servers, experiments, and jobs for distributed deep-learning training on Azure. Operators model their training environment as workspaces containing clusters and file servers, then submit experiments and jobs that run on the cluster, with operations to terminate jobs and list output files for inspection.

29 endpoints

CognitiveServicesManagementClient

Jentic publishes the only available OpenAPI specification for CognitiveServicesManagementClient, keeping it validated and agent-ready. The Cognitive Services Management Client is the Azure Resource Manager API for provisioning and administering Cognitive Services accounts under a subscription. It exposes 14 endpoints for creating accounts, regenerating subscription keys, listing available SKUs by location, checking domain name availability, and enumerating supported account kinds. Use it to spin up Vision, Language, Speech, or Decision resource accounts and rotate their keys without touching the Azure Portal.

14 endpoints

Content Moderator Client

Jentic publishes the only available OpenAPI specification for Content Moderator Client, keeping it validated and agent-ready. The Azure Content Moderator API scans user-generated text and images for adult, racy, profane, or otherwise unsafe content and returns either a verdict or a routed review item. Across 35 endpoints it covers image moderation (with limits of 128px minimum and 4MB max), text moderation (1024 character maximum), custom term and image lists for blocklists with refreshable indexes, OCR, face detection in images, and a review workflow that hands flagged content to human moderators. Use it to gate UGC ingest, build a moderation queue, or detect specific terms before publishing.

35 endpoints

Execution Service

Jentic publishes the only available OpenAPI specification for the Azure Machine Learning Execution Service, keeping it validated and agent-ready. This narrow API starts and cancels training runs against an Azure Machine Learning workspace experiment. It supports starting a remote run, starting a run from a snapshot on a remote compute target, starting a local run, and cancelling a run by run id.

4 endpoints

Form Recognizer Client

Jentic publishes the only available OpenAPI specification for the Azure Cognitive Services Form Recognizer client (v2.0 preview), keeping it validated and agent-ready. The API extracts structured key-value pairs and tables from forms, layouts, and prebuilt receipts. It supports training custom models on your own labelled forms, listing and deleting those models, analysing forms with a custom model, analysing the layout of any document, and analysing receipts with the prebuilt receipt model. Analysis calls are asynchronous and return a result id you poll until the operation completes.

10 endpoints

HyperDrive

Jentic publishes the only available OpenAPI specification for HyperDrive, keeping it validated and agent-ready. HyperDrive is the Azure Machine Learning hyperparameter tuning service that submits and tracks parallel training runs against an ARM-scoped workspace. The API exposes two operations under /hyperdrive/v1.0/{armScope}/runs to launch a new tuning experiment and to cancel an in-flight run by runId.

2 endpoints

Machine Learning Compute Management Client

Jentic publishes the only available OpenAPI specification for Machine Learning Compute Management Client, keeping it validated and agent-ready. This API manages the legacy Microsoft.MachineLearningCompute provider — the operationalization clusters that hosted scoring endpoints for Azure ML models in the original AML Workbench era. It provisions clusters, retrieves their keys, refreshes system service updates, and inspects update availability. It does not train models or run scoring requests.

10 endpoints

Machine Learning Workspaces Management Client

Jentic publishes the only available OpenAPI specification for Machine Learning Workspaces Management Client, keeping it validated and agent-ready. This API manages Microsoft.MachineLearning workspaces — the legacy 'classic' Azure Machine Learning Studio workspace resource, which contains experiments, datasets, and web service plans. It exposes workspace provisioning, key listing, and resync via Azure Resource Manager. It does not run experiments or manage Azure Machine Learning v2 workspaces.

9 endpoints

ML Team Account Management Client

Jentic publishes the only available OpenAPI specification for Azure ML Team Account Management, keeping it validated and agent-ready. The Microsoft.MachineLearningExperimentation Resource Provider manages the legacy Team Accounts hierarchy of Azure Machine Learning Workbench: an account contains workspaces, and each workspace contains projects that hold experiment runs and assets. The control plane exposed here covers full lifecycle for accounts, workspaces, and projects, including SKU and tag updates. Although Workbench has been superseded by the modern Azure Machine Learning Workspace, organizations still operating on this preview can drive provisioning, RBAC scoping, and decommissioning entirely through the API.

17 endpoints

QnAMaker Runtime Client

Jentic publishes the only available OpenAPI specification for QnAMaker Runtime Client, keeping it validated and agent-ready. The runtime API serves answers from a published Azure QnA Maker knowledge base — given a user question and the knowledge base ID, it returns the best matching question-and-answer pair along with a confidence score. It also exposes a training endpoint that ingests user feedback so the knowledge base can refine its ranking over time.

2 endpoints

Run History APIs

Jentic publishes the only available OpenAPI specification for the Azure Machine Learning Run History APIs, keeping it validated and agent-ready. The Run History service tracks experiments, runs, metrics, artifacts, events, and tags inside an Azure Machine Learning workspace, giving teams a queryable record of every training and evaluation execution. Agents can query runs by tag or experiment, log metrics in batches, list run children for hierarchical workflows, and fetch artifact metadata for downstream packaging or serving. It is the system of record behind Azure ML experiment tracking.

26 endpoints

SearchIndexClient

Jentic publishes the only available OpenAPI specification for SearchIndexClient, keeping it validated and agent-ready. The Azure Cognitive Search data plane API queries and updates documents inside a search index, supporting full-text search, filtering, faceting, suggesters, autocomplete, and bulk indexing actions. Agents can run queries against an index, page through results, retrieve a single document by key, and push batches of upload, merge, or delete actions. This is the runtime API for any application that reads from or writes to a Cognitive Search index.

9 endpoints

SearchServiceClient

Jentic publishes the only available OpenAPI specification for SearchServiceClient, keeping it validated and agent-ready. The Azure Cognitive Search data plane management API defines the indexes, indexers, datasources, skillsets, and synonym maps that power a search service. Agents can create or update an index schema, configure an indexer to pull documents from a datasource, attach a skillset for AI enrichment, and trigger or reset indexer runs. It complements SearchIndexClient by configuring the structures that queries run against.

31 endpoints

Connect Your Agent

Step 1: 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

Step 2: 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.