Create compute targets for experiments and training
Define event-based retraining triggers
Automate model retraining based on new data additions or data changes
Train a model by using Python SDKv2
Trigger an Azure Machine Learning job, including from Azure DevOps or GitHub
Access and wrangle data during interactive development
Invoke the batch endpoint to start a batch scoring job
Define parameters for a job
Monitor compute utilization
Test an online deployed service
Track model training by using MLflow
Configure an environment for a job run
Deploy a model to a batch endpoint
Configure attached compute resources, including Apache Spark pools