Create and Publish Pipelines for Batch Inferencing with Azure

This course will teach you how to use the Azure Machine Learning service to build and run ML pipelines using the drag-and-drop designer interface. You will cover the publishing and deployment of pipelines for batch and real-time inferences.

A machine learning model goes through a number of stages in its lifecycle; from training, to evaluation, through deployment and then maintenance. While there are a number of tools available for these stages, their management can become overwhelming even for the seasoned ML engineer.

In this course, Create and Publish Pipelines for Batch Inferencing with Azure, you'll experience an intuitive and easy-to-maintain environment for all things ML and focus on building and running pipelines for batch inferences:

In this Azure tutorial, you will get an overview of the Azure ML service, learn about a number of data transformations, and how to use the pipeline to make predictions.

Azure Machine Learning is a cloud-based environment you can use to train, deploy, automate, manage, and track ML models.

This course is for anyone who wants to learn Azure ML and create and publish their own pipelines for batch and real-time inferences.

A machine learning model is a file that has been trained to recognize certain types of patterns. You train a model over a set of data, providing it an algorithm that it can use to reason over and learn from certain data.

Data pipelines are sets of data processing elements connected in a series where the output of one element is the input of the next one.