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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.
Course info
Level
Intermediate
Updated
Apr 22, 2021
Duration
2h 44m
Table of contents
Description
Course info
Level
Intermediate
Updated
Apr 22, 2021
Duration
2h 44m
Description

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:

  1. discover the Azure ML service and the breadth of features it has to offer when it comes to building and managing ML models
  2. explore a number of data transformations which can be applied to a dataset by simply dragging and dropping various modules into the pipeline
  3. see that the handling of missing values, the standardization of numeric features as well as one-hot encoding for categorical fields can all be accomplished without writing a line of code
  4. use the pipeline to make predictions on new data
Once you have finished this course, you will have a clear understanding of the capabilities of Azure ML and specifically its designer when it comes to defining and managing pipelines - which can be used for both training and inferencing.
Course FAQ
Course FAQ
What will I learn in this Azure tutorial?

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.

What is Azure Machine Learning?

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

Who is this Azure tutorial for?

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

What is a machine learning model?

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.

What are data pipelines?

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.

About the author
About the author

An engineer at heart, I am drawn to any interesting technical topic. Big Data, ML and Cloud are presently my topics of interest.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, and welcome to this course, Create and Publish Pipelines for Batch Inferencing with Azure. Let me start by introducing myself. I have a master's degree in computer science from Columbia University and have previously worked in companies such as Deutsche Bank and WebMD in New York. I presently work for Loonycorn, a studio for high‑quality video content. A machine learning model goes through a number of stages in its lifecycle from training, evaluation, deployment, and then maintenance. While there are a number of tools available for these, the management can become overwhelming. Azure ML, however, provides an intuitive and easy‑to‑maintain environment for all things ML. And in this course, we focus on building and running pipelines for batch inferences. We begin with an overview of the Azure ML service, including a number of its model building and management features. This includes the use of the Azure ML designer, which allows us to construct a pipeline to train a model and then apply it for inferences. We move on then to a number of data transformations, which can be applied to a dataset. We will see that the handling of missing values, the standardization of numeric features, as well as one hot encoding for categorical fields can all be accomplished without writing a line of code. Finally, we move on to the use of the pipeline to make predictions on new data. You will see how just a few modifications to the training pipeline are needed to accomplish this. The pipeline can then be used for batch inferences, as well as real‑time predictions. Once you have finished this course, you will have a clear understanding of the capabilities of Azure ML and specifically its designer when it comes to defining and managing pipelines.