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

Analyzing model errors

Error analysis is a model assessment/debugging tool that enables you to gain a deeper understanding of your machine learning model errors. Error analysis helps you identify cohorts within your dataset with higher error rates than the rest of the records. You can observe the misclassified and erroneous data points more closely to investigate whether any systematic patterns can be spotted, such as whether no data is available for a specific cohort. Error analysis is also a powerful way to describe the current shortcomings of the system and communicate that to other stakeholders and auditors.

The tool consists of several visualization components that can help you understand where the errors appear.

Navigate to the Author | Notebooks section of your Azure Machine Learning Studio web interface and open the chapter10.ipynb notebook. From Menu, in the Editors sub-menu, click Edit in Jupyter to open the same notebook in Jupyter and continue editing it there, as...