Book Image

Azure Data Scientist Associate Certification Guide

By : Andreas Botsikas, Michael Hlobil
Book Image

Azure Data Scientist Associate Certification Guide

By: Andreas Botsikas, Michael Hlobil

Overview of this book

The Azure Data Scientist Associate Certification Guide helps you acquire practical knowledge for machine learning experimentation on Azure. It covers everything you need to pass the DP-100 exam and become a certified Azure Data Scientist Associate. Starting with an introduction to data science, you'll learn the terminology that will be used throughout the book and then move on to the Azure Machine Learning (Azure ML) workspace. You'll discover the studio interface and manage various components, such as data stores and compute clusters. Next, the book focuses on no-code and low-code experimentation, and shows you how to use the Automated ML wizard to locate and deploy optimal models for your dataset. You'll also learn how to run end-to-end data science experiments using the designer provided in Azure ML Studio. You'll then explore the Azure ML Software Development Kit (SDK) for Python and advance to creating experiments and publishing models using code. The book also guides you in optimizing your model's hyperparameters using Hyperdrive before demonstrating how to use responsible AI tools to interpret and debug your models. Once you have a trained model, you'll learn to operationalize it for batch or real-time inferences and monitor it in production. By the end of this Azure certification study guide, you'll have gained the knowledge and the practical skills required to pass the DP-100 exam.
Table of Contents (17 chapters)
Section 1: Starting your cloud-based data science journey
Section 2: No code data science experimentation
Section 3: Advanced data science tooling and capabilities

Chapter 10: Understanding Model Results

In this chapter, you will learn how to analyze the results of your machine learning models to interpret why the model made the inference it did. Understanding why the model predicted a value is the key to avoiding black box model deployments and to be able to understand the limitations your model may have. In this chapter, you will learn about the available interpretation features of Azure Machine Learning and visualize the model explanation results. You will also learn how to analyze potential model errors and detect cohorts where the model is performing poorly. Finally, you will explore tools that will help you assess your model's fairness and allow you to mitigate potential issues.

In this chapter, we're going to cover the following topics:

  • Creating responsible machine learning models
  • Interpreting the predictions of the model
  • Analyzing model errors
  • Detecting potential model fairness issues