AI Platform Training & Prediction API

Name: AI Platform Training & Prediction API API
Brand: AI Platform Training & Prediction API
Availability: InStock

✓ Official Vendor SpecAI/MLMl Inferenceoauth229 EndpointsREST

For Agents

Train custom ML models, deploy versions, and serve online predictions or explanations from the original AI Platform Training & Prediction service.

Quickstart

Get started with AI Platform Training & Prediction API in minutes using your preferred integration method.

# Add to your MCP client config (Claude Desktop, Cursor, Windsurf)
{
  "jentic": {
    "url": "https://api.jentic.com/mcp",
    "auth": "oauth"
  }
}

# Then ask your agent:
"run an online prediction on google ai platform"

# → Jentic returns the GET /events tool with parameter schema, agent executes.

Capabilities

What an agent can do with AI Platform Training & Prediction API API.

Run online prediction calls against deployed AI Platform model versions

Run online explain calls to surface feature attributions for a prediction

Manage AI Platform models and their versions with default-version selection

Submit and cancel training and batch prediction jobs

GET STARTED

Start building with AI Platform Training & Prediction API API

Explore with Jentic

View OpenAPI Spec

Use for: Run an online prediction against an AI Platform model version, Get feature attributions for a prediction via the explain endpoint, Set a model version as the default for a model, Submit a hyperparameter trial measurement to an existing study

Not supported: Does not handle Vertex AI Pipelines, Matching Engine, or generative AI endpoints — use for AI Platform Training & Prediction models, versions, jobs, and hyperparameter trials only.

AI Platform Training & Prediction is Google Cloud's earlier-generation managed service for training custom machine learning models and serving online and batch predictions. The API exposes models, model versions, jobs, online predict and explain calls, hyperparameter trial measurement, and study management. Most teams new to Google Cloud now use the Vertex AI API for the same workloads, but AI Platform remains supported for existing pipelines.

Use Cases

Patterns agents use AI Platform Training & Prediction API API for, with concrete tasks.

★ Online prediction serving

Deploy a trained model to AI Platform and call the predict endpoint to score requests in real time. The endpoint accepts JSON instances and returns the model's predictions, with autoscaling and version routing handled by the platform. Useful for teams maintaining existing AI Platform deployments rather than migrating to Vertex AI.

Call predict on projects/acme/models/churn/versions/v3 with a JSON payload of customer feature instances and capture the prediction array

Hyperparameter tuning trials

Drive a hyperparameter study by submitting trial measurements via addMeasurement, marking trials complete, and consulting checkEarlyStoppingState to abort underperforming trials. The study and trial endpoints support custom search algorithms running outside AI Platform that still want to use the platform's coordination primitives.

Add a measurement to trial projects/acme/locations/us-central1/studies/lr-search/trials/12 with stepCount 50 and metrics validation_loss=0.42, then check early stopping state

Model explainability for compliance

Use the explain endpoint alongside predict so that customer-facing decisions can be paired with feature attributions, useful in regulated domains where every model decision must be auditable. Attributions reflect the explainability config attached to the model version.

Call explain on projects/acme/models/credit/versions/v2 with the same instance payload used for predict and return both prediction and attributions

Agent integration via Jentic

AI agents that orchestrate ML pipelines on legacy AI Platform deployments can use this API through Jentic to run predictions, manage versions, and drive hyperparameter searches without holding the OAuth refresh token in their prompt.

List versions of model projects/acme/models/churn and set the version with the highest deployedAt timestamp as default via setDefault

Key Endpoints

29 endpoints — ai platform training & prediction is google cloud's earlier-generation managed service for training custom machine learning models and serving online and batch predictions.

METHOD

PATH

DESCRIPTION

POST

/v1/{+name}:predict

Run an online prediction

POST

/v1/{+name}:explain

Run an online explain call

POST

/v1/{+name}:setDefault

Set a model version as default

POST

/v1/{+name}:addMeasurement

Add a hyperparameter trial measurement

POST

/v1/{+name}:complete

Mark a hyperparameter trial complete

POST

/v1/{+name}:checkEarlyStoppingState

Check trial early stopping state

POST

/v1/{+name}:cancel

Cancel a job or operation

GET

/v1/{+name}:getConfig

Read AI Platform config for a project

POST

/v1/{+name}:predict

Run an online prediction

POST

/v1/{+name}:explain

Run an online explain call

POST

/v1/{+name}:setDefault

Set a model version as default

POST

/v1/{+name}:addMeasurement

Add a hyperparameter trial measurement

POST

/v1/{+name}:complete

Mark a hyperparameter trial complete

Why though Jentic?

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

Credential isolation

Google Cloud OAuth 2.0 client credentials and refresh tokens are stored encrypted in the Jentic vault. Agents receive short-lived bearer tokens scoped to cloud-platform; the refresh token and client secret never enter the agent's context.

Intent-based discovery

Agents search Jentic by intent such as run online prediction or hyperparameter trial measurement and Jentic returns the matching operations with input schemas, so the agent does not have to read Google Cloud's discovery documents.

Time to first call

Direct integration with AI Platform takes 1-2 days for OAuth setup, version targeting, and trial coordination logic. Through Jentic the same workflows are under an hour: search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

Vertex AI API

Successor unified ML platform that replaces AI Platform for new workloads

Choose Vertex AI for new ML projects; use AI Platform when the agent maintains an existing deployment that has not been migrated

Complementary

Notebooks API

Managed JupyterLab notebooks used to develop the models served by AI Platform

Use Notebooks for model development; use AI Platform to serve the resulting models

Complementary

Dataflow API

Streaming and batch pipelines that feed features and predictions in and out of AI Platform

Use Dataflow for feature pipelines around AI Platform; use AI Platform for the prediction itself

FAQs

Specific to using AI Platform Training & Prediction API API through Jentic.

What authentication does the AI Platform API use?

The API uses Google OAuth 2.0 with the https://www.googleapis.com/auth/cloud-platform scope. Through Jentic, OAuth credentials are held in the encrypted vault and the agent receives only a short-lived bearer token, never the refresh token.

Can I get feature attributions alongside predictions?

Yes, the explain endpoint returns attributions according to the explainability config attached to the model version. It accepts the same instance payload as predict, so you can call both for the same request when you need an auditable decision.

What are the rate limits for the AI Platform API?

Online prediction has its own per-project queries-per-minute quota separate from training and batch jobs, listed under the AI Platform service in the Cloud Console quotas page. High-throughput serving usually requires requesting a quota increase.

How do I run an online prediction through Jentic?

Search Jentic for run online prediction on ai platform, load the predict operation that maps to /v1/{+name}:predict, and execute with the model version resource name and a JSON instances payload. Jentic injects the OAuth bearer for the call.

Should I be using Vertex AI instead?

For new workloads, Google recommends Vertex AI, which supersedes AI Platform with a unified API for training, prediction, pipelines, and matching engine. AI Platform remains supported for existing deployments and is the right choice when you are not yet ready to migrate.

Is the AI Platform API free?

The API itself is not separately billed; you pay for prediction node-hours, training compute, and Vizier study usage. See the AI Platform pricing page for current rates by region and machine type.