Models
Cloudera AI Project Lifecycle
Models
Models - Concepts and Terminology
Challenges with Machine Learning in production
Challenges with model deployment and serving
Challenges with model monitoring
Challenges with model governance
Model visibility
Model explainability, interpretability, and reproducibility
Model governance using Apache Atlas
Using Cloudera AI Registry
Setting up Cloudera AI Registry
Prerequisites for creating Cloudera AI Registry
Creating a Cloudera AI Registry
Synchronizing Cloudera AI Registry with a workbench
Viewing details for Cloudera AI Registries
Cloudera AI Registry permissions
Model access control
Deleting Cloudera AI Registry
Force delete a Cloudera AI Registry
Registering and deploying models with Cloudera AI Registry
Creating a model using MLflow
Registering a model using the Cloudera AI Registry user interface
Registering a model using MLflow SDK
Using MLflow SDK to register customized models
Creating a new version of a registered model
Deploying a model from the Cloudera AI Registry page
Deploying a model from the Cloudera AI Registry using APIv2
Deploying a model from the destination Project page
Viewing Details for Cloudera AI Registry
Delete a model from Cloudera AI Registry
Disabling Cloudera AI Registry
Creating and deploying a Model
Hosting an LLM as a Cloudera AI Workbench model
Deploying the Cloudera AI Workbench model
Usage guidelines for deploying models with Cloudera AI
Known Issues and Limitations with Model Builds and Deployed Models
Request/Response Formats (JSON)
Testing calls to a Model
Securing Models
Access Keys for Models
API Key for Models
Enabling authentication
Generating an API key
Managing API Keys
Workflows for active Models
Technical metrics for Models
Debugging issues with Models
Deleting a Model
Example - Model training and deployment (Iris)
Training the Model
Deploying the Model