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Pipelines and Jobs in Azure ML

Tomaz Kastrun continues an advent on Azure ML. Day 11 covers pipelines:

A pipeline is set of instructions (or a workflow) for executing particular work of a machine learning task. The idea behind pipelines is that will help the team of data scientists and machine learning engineers standardize workflow and incorporate best practices of preparing data, producing training models, executing the models and deploying them. Pipelines will help improve and build workflow efficiently and in such a way that it can be reusable.

And the idea behind it, is to split a machine learning process into smaller tasks, a multistep workflow, where each step is a separate component than can be developed, upgraded, optimised, configured, automated, and deleted separately. And these steps, connected through interfaces, form a workflow.

Day 12 makes us get a job:

An Azure ML job executes a task against a specified compute target. This is also how the job is created. By configuring a new job, you can also scale out model training, since there are single node and distributed training available.

A simple job command would be to execute a command in a Docker container. And further parameter sweeping can be executed, by specifying it in the job itself.