DP-100 Designing and Implementing a Data Science Solution on Azure

Define early termination options

Configure job run settings for a script

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

Create a pipeline

Pass data between steps in a pipeline

Run and schedule a pipeline

Monitor pipeline runs

Create custom components

Use component-based pipelines

Describe MLflow model output
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