Amazon Lookout for Vision API

Name: Amazon Lookout for Vision API API
Brand: Amazon Lookout for Vision API
Availability: InStock

✓ Official Vendor SpecAI/MLVisionapiKey22 EndpointsREST

For Agents

Train and run computer-vision anomaly-detection models on industrial imagery, then deploy them to the cloud or to edge devices for defect detection.

Quickstart

Get started with Amazon Lookout for Vision 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:
"detect anomalies on a product image"

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

Capabilities

What an agent can do with Amazon Lookout for Vision API API.

Create and manage Lookout for Vision projects that scope training and inference resources

Build and update training and test datasets from labelled images of normal and anomalous parts

Train, list, and describe anomaly-detection models per project version

Start and stop a model to make it available for synchronous inference

GET STARTED

Start building with Amazon Lookout for Vision API API

Explore with Jentic

View OpenAPI Document

Use Cases

Patterns agents use Amazon Lookout for Vision API API for, with concrete tasks.

★ Production-Line Defect Detection

Manufacturers deploy Lookout for Vision to flag defective parts on the production line by sending captured images to DetectAnomalies and acting on the response. Operators train a model on labelled normal and anomalous images, host it as a project version, and integrate the inference endpoint into the line's PLC or edge device, replacing manual visual inspection that struggles with throughput and consistency.

Call POST /2020-11-20/projects/{projectName}/models/{modelVersion}/detect with the image bytes for a part captured on the line and return the IsAnomalous flag and confidence

Model Lifecycle Management

ML engineers manage the project, dataset, and model lifecycle through the Lookout for Vision API: create projects, attach training and test datasets, kick off training jobs, and start or stop hosted models to balance inference availability against cost. The API surfaces every step of CreateProject, CreateDataset, CreateModel, StartModel, and StopModel programmatically.

Create a project named 'pcb-quality', attach training and test datasets from S3, start training, and poll DescribeModel until status reaches HOSTED

Edge Deployment Packaging

When inference must run on-device with low latency, teams package a trained Lookout for Vision model through the model-packaging-jobs endpoint for AWS IoT Greengrass deployment. The packaged model runs locally on factory hardware while training and updates remain centralised in AWS.

Call the model-packaging endpoint for project {projectName} model version {modelVersion} with a Greengrass component spec, then poll the packaging job status until SUCCEEDED

Agent-Driven Quality Inspection via Jentic

AI agents embedded in operations dashboards call Lookout for Vision through Jentic to surface inspection results, trigger model retraining when drift is detected, and orchestrate dataset updates as new examples are labelled. Jentic exposes the 22 Lookout for Vision operations as discoverable tools so the agent picks DetectAnomalies, StartModel, or CreateModel based on the intent.

Given an alert 'inspection accuracy dropped on line 3', search Jentic for 'create dataset Lookout for Vision', upload a new labelled batch, and trigger retraining of the affected project's model

Key Endpoints

22 endpoints — amazon lookout for vision is the aws service for detecting visual defects in industrial products using computer vision.

METHOD

PATH

DESCRIPTION

GET

/2020-11-20/projects

List Lookout for Vision projects

POST

/2020-11-20/projects/{projectName}/datasets

Create a dataset under a project

POST

/2020-11-20/projects/{projectName}/models

Train a new model version

GET

/2020-11-20/projects/{projectName}/models/{modelVersion}

Describe a model version

POST

/2020-11-20/projects/{projectName}/models/{modelVersion}/detect

Run anomaly detection on an image

GET

/2020-11-20/projects/{projectName}/modelpackagingjobs/{jobName}

Describe a model packaging job

GET

/2020-11-20/projects

List Lookout for Vision projects

POST

/2020-11-20/projects/{projectName}/datasets

Create a dataset under a project

POST

/2020-11-20/projects/{projectName}/models

Train a new model version

GET

/2020-11-20/projects/{projectName}/models/{modelVersion}

Describe a model version

POST

/2020-11-20/projects/{projectName}/models/{modelVersion}/detect

Run anomaly detection on an image

Why though Jentic?

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

Credential isolation

AWS access keys, secret keys, and optional session tokens are stored encrypted in the Jentic vault. Jentic constructs the SigV4 signature on each request — raw AWS secrets never enter the agent's context or transcripts.

Intent-based discovery

Agents search by intent (e.g., 'detect anomalies on an image' or 'train a vision model') and Jentic returns the matching Lookout for Vision operation from the 22 available, with full request and response schemas including project and model-version path parameters.

Time to first call

Direct AWS Lookout for Vision integration: 3-5 days for SigV4 signing, dataset wiring, training-job polling, and inference plumbing. Through Jentic: under 1 hour — search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

Google Cloud Vision

Google Cloud Vision offers general-purpose image classification and object detection without Lookout for Vision's industrial-anomaly focus.

Choose Cloud Vision for broad image understanding; choose Lookout for Vision when the task is purpose-built defect detection on industrial parts.

Alternative

Google Cloud AutoML Vision

AutoML Vision lets teams train custom image classifiers with similar low-code training flow but without integrated edge packaging for industrial use.

Choose AutoML Vision when the team is already on Google Cloud; choose Lookout for Vision when AWS Greengrass edge deployment is required.

Complementary

Cloudinary Upload API

Use Cloudinary to manage and serve the source images that feed into Lookout for Vision training and inference.

Use Cloudinary when image storage and transformation matter; pass the resulting URLs or bytes into Lookout for Vision for inspection.

FAQs

Specific to using Amazon Lookout for Vision API API through Jentic.

What authentication does the Amazon Lookout for Vision API use?

Lookout for Vision uses AWS Signature Version 4. The Authorization header on each request must be a SigV4 signature derived from the IAM access key, secret key, and optional session token. Through Jentic, AWS credentials live in the Jentic vault and Jentic constructs the SigV4 signature for the agent on each call.

Can I run defect detection on an image with the Lookout for Vision API?

Yes. POST /2020-11-20/projects/{projectName}/models/{modelVersion}/detect runs synchronous inference on the image bytes you supply and returns whether the part is anomalous, the confidence score, and (for segmentation models) anomaly masks per defect type. The model must be in HOSTED state before calling DetectAnomalies.

What are the rate limits for the Amazon Lookout for Vision API?

Lookout for Vision applies per-account, per-region request quotas published in the AWS service quotas console. DetectAnomalies typically supports tens of transactions per second per hosted model, with higher limits available on request. Throttling returns HTTP 400 with a ThrottlingException — back off using exponential retry.

How do I start a hosted model through Jentic?

Search Jentic for 'start a Lookout for Vision model', load the schema for POST /2020-11-20/projects/{projectName}/models/{modelVersion}/start, and execute. After the call, poll GET /2020-11-20/projects/{projectName}/models/{modelVersion} until status is HOSTED before invoking DetectAnomalies.

How is Lookout for Vision priced?

AWS bills Lookout for Vision based on training hours, hosted-inference hours per active model, and DetectAnomalies image volume. Stopping a hosted model with the StopModel operation halts the inference-hour charges — agents that only run inference during shifts should stop the model overnight to save cost.

Can I deploy Lookout for Vision models to edge devices?

Yes. The model-packaging-jobs endpoints let you package a trained model as an AWS IoT Greengrass component so inference runs on-device for low-latency factory environments. Training and dataset management remain in AWS while inference happens locally.