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Microsoft

Course 40577-G: Microsoft Cloud Workshop: Innovate and modernize apps with Data and AI

  • Duration: 1 days
  • Job Role: Data Engineer

Course 40577-G: Microsoft Cloud Workshop: Innovate and modernize apps with Data and AI

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In this workshop, you will look at the process of implementing a modern application with Azure services. The workshop will cover event sourcing and the Command and Query Responsibility Segregation (CQRS) pattern, data loading, data preparation, data transformation, data serving, anomaly detection, creation of a predictive maintenance model, and real-time scoring of a predictive maintenance model.

Audience Profile

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.

Prerequisites

  • Workshop content presumes 300-level of architectural expertise of infrastructure and solutions design. We suggest students take this prerequisite prior to attending this workshop.

Course outline

Module 1: Whiteboard Design Session - Innovate and modernize apps with Data and AI

Module Overview

In this module, the students will learn how to design a solution for ingesting and preparing manufacturing device sensor data, as well as detecting anomalies in sensor data and creating, training, and deploying a machine learning model which can predict when device maintenance will become necessary.

Lessons

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

Lab Sessions

Not available for this module

Lab Lessons

Lab lessons not available

After completing this module, students will be able to:

Knowledge on innovate and modernize apps with Data and AI Whiteboard Design Session.

Module 2: Hands-On Lab - Innovate and modernize apps with Data and AI

Module Overview

In this module, the students will learn how to build a cloud processing and machine learning solution for IoT data. The students will begin by deploying a factory load simulator using Azure IoT Edge to write into Azure IoT Hub, following the recommendations in the Azure IoT reference architecture. The data in this simulator represents sensor data collected from a stamping press machine, which cuts, shapes, and imprints sheet metal. The rest of the module will show how to implement an event sourcing architecture using Azure technologies ranging from Cosmos DB to Stream Analytics to Azure Functions to Azure Database for PostgreSQL.
Using factory-generated data,the students will learn how to use the Anomaly Detection service built into Stream Analytics to observe and report on abnormal machine temperature readings, also student will learn how to apply historical machine temperature and stamping pressure values in the creation of a machine learning model to identify potential issues which might require machine adjustment, also will deploy this predictive maintenance model and generate predictions on simulated stamp press data.

Lessons

Deploy a factory load simulator
Use Azure Machine Learning to train and register a predictive maintenance model
Create an Azure Function to send event telemetry to Cosmos DB
Enrich event telemetry with predictive maintenance results
Enrich event telemetry with automated anomaly detection
Send scored telemetry data to PostgreSQL
Modernize services logic to use event sourcing and CQRS
View the factory status in a Power BI report

Lab Sessions

Not available for this module

Lab Lessons

Lab lessons not available

After completing this module, students will be able to:

Knowledge on innovate and modernize apps with Data and AI Hands-On Lab.

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