Expanded

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. First, you'll discover the Azure ML service and the breadth of features it has to offer when it comes to building and managing ML models. Then, you'll explore a number of data transformations which can be applied to a dataset by simply dragging and dropping various modules into the pipeline. Next, you'll 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, you'll 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.

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.

More from the author
Design Data Models for Couchbase
Beginner
2h 7m
Sep 29, 2020
Recognize the Need for Document Databases
Beginner
1h 40m
Sep 18, 2020
More courses by Kishan Iyer
Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
[Autogenerated] Hi and welcome to this course, create and publish pipelines for batch influencing 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 Web MD 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 life cycle from training, evaluation, deployment and then maintenance. While there are a number of tools available for these, a management can become overwhelming. Azure XAML 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 youth 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 data set. 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 youth 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.