Configure compute for a job run
Consume data from a data asset in a job
Run a script as a job by using Azure Machine Learning
Use MLflow to log metrics from a job run
Use logs to troubleshoot job run errors
Configure an environment for a job run
Define parameters for a job
Pass data between steps in a pipeline
Run and schedule a pipeline
Use component-based pipelines
Describe MLflow model output
Identify an appropriate framework to package a model
Assess a model by using responsible AI guidelines