Experiments
Experiments with MLflow
Cloudera AI Experiment Tracking through MLflow API
Running an Experiment using MLflow
Visualizing Experiment Results
Deploying an MLflow model as a Cloudera AI Workbench Model (Legacy)
Using an MLflow Model Artifact in a Model REST API
Registering and deploying models with Cloudera AI Registry
Creating a model using MLflow
Registering a model using the 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 AI Registry page
Deploying a model from the Cloudera AI Registry using APIv2
Deploying a model from the destination Project page
Viewing and synchronizing the Cloudera AI Registry instance
Deleting a model from Cloudera AI Registry
Disabling Cloudera AI Registry
Automatic Logging
Setting Permissions for an Experiment
MLflow transformers
Evaluating LLM with MLflow
Using Heuristic-based metrics
Using LLM-as-a-Judge metrics
Known issues and limitations