Communities
A collaborative platform to connect and grow with like-minded Informaticans across the globe
Product Communities
Connect and collaborate with Informatica experts and champions
Discussions
Have a question? Start a Discussion and get immediate answers you are looking for
User Groups
Customer-organized groups that meet online and in-person. Join today to network, share ideas, and get tips on how to get the most out of Informatica
Get Started
Community Guidelines
Knowledge Center
Troubleshooting documents, product guides, how to videos, best practices, and more
Knowledge Base
One-stop self-service portal for solutions, FAQs, Whitepapers, How Tos, Videos, and more
Support TV
Video channel for step-by-step instructions to use our products, best practices, troubleshooting tips, and much more
Documentation
Information library of the latest product documents
Velocity (Best Practices)
Best practices and use cases from the Implementation team
Learn
Rich resources to help you leverage full capabilities of our products
Trainings
Role-based training programs for the best ROI
Certifications
Get certified on Informatica products. Free, Foundation, or Professional
Product Learning Paths
Free and unlimited modules based on your expertise level and journey
Resources
Library of content to help you leverage the best of Informatica products
Tech Tuesdays Webinars
Most popular webinars on product architecture, best practices, and more
Product Availability Matrix
Product Availability Matrix statements of Informatica products
SupportFlash
Monthly support newsletter
Support Documents
Informatica Support Guide and Statements, Quick Start Guides, and Cloud Product Description Schedule
Product Lifecycle
End of Life statements of Informatica products
Ideas
Events
Change Request Tracking
Marketplace
English
  • Administrator
  • All Products

Table of Contents

Search

  1. Preface
  2. Informatica Intelligent Cloud Services REST API
  3. Platform REST API version 2 resources
  4. Platform REST API version 3 resources
  5. Data Integration REST API
  6. File Ingestion and Replication REST API
  7. Streaming Ingestion and Replication REST API
  8. Model Serve REST API
  9. RunAJob utility
  10. ParamSetCli utility
  11. REST API codes
  12. REST API resource quick references

REST API Reference

REST API Reference

Back Next

Model Serve resource quick reference

Model Serve
 resource quick reference

The following list contains the syntax and a brief description of the actions you can perform with the
Model Serve
 resources:
Get descriptions of all quick start models
To get a list of the names and descriptions of all of the quick start models, use the following URI: 
/mlops/api/v1/modelhub/models
Monitor all quick start models
To get the statuses of and information about all of the quick start models, use the following URI: 
/mlops/api/v1/modelhub/monitor
Get information about a quick start model
To get the status of and information about a single quick start model, use the following URI: 
/mlops/api/v1/modelhub/preBuiltModel?name=<model name>
Use
TEXT_TRANSLATION
 or
IMAGE_CLASSIFICATION
 as the model name.
Get the code template file
To get the code template file that you need to define a machine learning model, use the following URI: 
/mlops/api/v1/model/template/custom
Upload a model file
When you upload a model file, first generate a model file ID and then use the ID to upload the file.
To generate a model file ID, use the following URI: 
/mlops/api/v1/model/upload/generateId
To upload or update a model file, use the following URI: 
/mlops/api/v1/model/upload/<model file ID>
Register a machine learning model
Register a machine learning model after you upload the model file that defines the machine learning algorithm.
To register a machine learning model, use the following URI: 
/frs/v1/Projects('<model ID>')/Documents
Edit a machine learning model
To edit a registered machine learning model, use the following URI: 
/frs/v1/Documents('<ID>')
Download a model file
To download the model file from a registered machine learning model, use the following URI: 
/mlops/api/v1/model/download/<ID>
Create a model deployment
To create a model deployment based on a registered machine learning model, use the following URI: 
/frs/v1/Projects('<deployment ID>')/Documents
Edit a model deployment
To edit a model deployment, use the following URI: 
/frs/v1/Documents('<deployment ID>')
Monitor deployments
To monitor the status of all model deployments in your organization, use the following URI: 
/mlops/api/v1/deployment/monitor
To monitor the status of one model deployment, use the following URI: 
/mlops/api/v1/deployment/monitor/<deployment ID>
Control a quick start model or model deployment
You can start a quick start model or model deployment to make it available for predictions, restart it while it's running, and stop it to release the resources.
To start a deployment, use the following URI: 
/mlops/api/v1/deployment/control/<deployment ID>/start
To restart a deployment, use the following URI: 
/mlops/api/v1/deployment/control/<deployment ID>/restart
To stop a deployment, use the following URI: 
/mlops/api/v1/deployment/control/<deployment ID>/stop
Generate predictions
To generate predictions from an available quick start model or model deployment, use the following URI: 
/mlops/api/v1/deployment/request/<deployment ID>
Delete assets
To delete a machine learning model or model deployment, use the following URI: 
/frs/api/v1/Documents('<ID>')
0 COMMENTS
We’d like to hear from you!