Using
Cloudera AI Inference service
Cloudera AI Inference service Overview
Key Features
Key Applications
Terminology
Limitations and Restrictions
Supported Model Artifact Formats
Cloudera AI Inference service Concepts
Authenticating Cloudera AI Inference service
Managing Model Endpoints using UI
Creating a Model Endpoint using UI
Listing Model Endpoints using UI
Viewing details of a Model Endpoint using UI
Editing a Model Endpoint Configuration using UI
Managing Model Endpoints using API
Preparing to interact with the Cloudera AI Inference service API
Creating a Model Endpoint using API
Listing Model Endpoints using API
Describing a Model Endpoint using API
Deleting a Model Endpoint using API
Autoscaling Model Endpoints using API
Tuning auto-scaling sensitivity using the API
Running Models on GPU
Deploying models with Canary deployment using API
Interacting with Model Endpoints
Making an inference call to a Model Endpoint with an OpenAI API
Cloudera AI Inference service using OpenAI Python SDK client in a Cloudera AI Workbench Session
Cloudera AI Inference service using OpenAI Python SDK client on a local machine
OpenAI Inference Protocol Using Curl
Making an inference call to a Model Endpoint with Open Inference Protocol
Open Inference Protocol Using Python SDK
Open Inference Protocol Using Curl
Deploying Predictive Models
Accessing Cloudera AI Inference service Metrics