Models
Machine Learning 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 Model Registry
Setting up Model Registry
Creating a Model Registry
Viewing Details for Model Registry
Model Registry permissions
Model access control
Deleting Model Registry
Registering and deploying a Model Registry
Creating a model using MLflow
Registering a model using the Model Registry user interface
Registering a model using MLflow SDK
Creating a new version of a registered model
Deploying a model from the Model Registry page
Deploying a model from the destination Project page
Viewing Details for Model Registry
Delete a model from Model Registry
Disabling Model Registry
Creating and Deploying a Model
Usage Guidelines
Known Issues and Limitations
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
Active Model Workflows
Technical Metrics for Models
Debugging Issues with Models
Deleting a Model
Example - Model Training and Deployment (Iris)
Train the Model
Deploy the Model