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Microsoft

Course 40559-G: Microsoft Cloud Workshop: MLOps

  • Duration: 1 days
  • Job Role: Data Engineer

Course 40559-G: Microsoft Cloud Workshop: MLOps

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In this workshop, you will learn how Trey Research can leverage Deep Learning technologies to scan through their vehicle specification documents to find compliance issues with new regulations, and manage the classification through their web application. The entire process from model creation, application packaging, model deployment and application deployment needs to occur as one unified repeatable, pipeline. At the end of this workshop, you will be better able to design and implement end-to-end solutions that fully operationalize deep learning models, inclusive of all application components that depend on the 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 “Summary” and “Skills gained” 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

  • Knowledge of cloud computing.

Course outline

Module 1: Whiteboard Design Session - MLOps
Module Overview

In this module, you will learn how Wide World Importers (WWI) can leverage Deep Learning technologies to scan through their vehicle specification documents to find compliance issues with new regulations and manage the classification thru their web application. The entire process from model creation, application packaging, model deployment and application deployment needs to occur as one unified repeatable, pipeline. At the end of this module, you will be better able to design and implement end-to-end solutions that fully operationalize deep learning models, inclusive of all application components that depend on the model.

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:

Describe case study.
Design concept solution.
Describe solution.

Module 2: Hands-on lab - MLOps
Module Overview

In this module, you will learn how Trey Research can leverage Deep Learning technologies to scan through their vehicle specification documents to find compliance issues with new regulations. You will standardize the model format to ONNX and observe how this simplifies inference runtime code, enabling pluggability of different models and targeting a broad range of runtime environments, and most importantly, improves inferencing speed over the native model. You will build a DevOps pipeline to coordinate retrieving the latest best model from the model registry, packaging the web application, deploying the web application, and inferencing web service. After a first successful deployment, you will make updates to both the model, the and web application, and execute the pipeline once to achieve an updated deployment. You will also learn how to monitor the model’s performance after it is deployed, so Trey Research can be proactive with performance issues. At the end of this module, you will be better able to implement end-to-end solutions that fully operationalize deep learning models, inclusive of all application components that depend on the model.

Lessons

Creating and evaluating compliance classification models
Registering the model
Setup New Project in Azure DevOps
Setup and Run the Build Pipeline
Setup the Release Pipeline
Test Build and Release Pipelines
Testing the deployed solution
Examining deployed model performance

Lab Sessions

Not available for this module

Lab Lessons

Lab lessons not available

After completing this module, students will be able to:

Understand how to design and implement end-to-end solutions that fully operationalize deep learning models.

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