Course 40561-G: Microsoft Cloud Workshop: Machine Learning

Course Modality

Instructor-led (classroom)

Course Level

Advanced

Course Time

1 day

Course Language

English

Course Overview

In this whiteboard design session, you will work with a group to design and implement a solution that combines Azure Databricks with Azure Machine Learning service to build, train and deploy the machine learning and deep learning models. You will learn how to use automated machine learning, model lifecycle management from training to deployment, in batch and real-time inferencing scenarios, and construct deep learning models for Natural Language Processing (NLP) in text classification and forecasting against time-series data. Finally, you’ll learn to compare data with PyTorch and Keras for deep learning.

Prerequisites

There are no prerequisites for this course.

Intended Audience

This workshop is intended for Cloud Architects and IT professionals who have architectural expertise of infrastructure and solutions design in cloud technologies and want to learn more about Azure and Azure services as described in the ‘About this Course’ and ‘At Course Completion’ areas. Those attending this workshop should also be experienced in other non-Microsoft cloud technologies, meet the course prerequisites, and want to cross-train on Azure.

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Online Practice Labs

Projects provide you with sample work to show prospective employers.

Applied Projects

Real-world projects relevant to what you’re learning throughout the program

Learner Social Forums

A support team focused on helping you succeed alongside a peer community

Skill Covered

After completing this course, students will understand:

  •  The capabilities and implementation solutions of Azure Machine Learning and Azure              Databricks.

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Course Curriculum

Whiteboard Design Session – Machine Learning

Lessons

  • Review the customer case study
  • Design a proof of concept solution
  • Present the solution

Hands-on lab – Machine Learning

Lessons

  • Creating a forecast model using automated machine learning
  • Understanding the automated ML generated forecast model using model explainability
  • Creating a deep learning model (RNN) for time series data and registering the model
  • Using a forecast model for the scoring of streaming telemetry
  • Creating a deep learning text classification model

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