Microsoft Exam DP-100 : Designing and Implementing a Data Science Solution on Azure

Paths

Microsoft Exam DP-100 : Designing and Implementing a Data Science Solution on Azure

Authors: Jared Rhodes, Janani Ravi, Ifedayo Bamikole, Axel Sirota, Kishan Iyer

Microsoft Azure offers a set of related services to address the day-to-day workflow of a data scientist. This skill teaches how these Azure services work together to enable... Read more

  • Set up an Azure Machine Learning Workspace
  • Optimize and Manage Models
  • Experimental Design for Data Analysis
  • Build optimal models With Azure Automated ML
  • Create and publish pipeline for batch inferencing with Azure

Pre-requisites

This path is intended for learners familiar with data science workflows and general principles, but who have never applied these on Microsoft Azure

Getting Started with Machine Learning using Azure

The courses in this section teach you how to get started with creating a machine learning workspace and to build a data pipeline . In addition, this part of the path discusses the role of effective communication as part of the data science workflow.

Building Your First Data Science Project in Microsoft Azure

by Jared Rhodes

Jun 19, 2020 / 1h 25m

1h 25m

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Description

The past five years have shown a boom in the data science field with advancements in hardware and cloud computing. In this course, Building Your First Data Science Project in Microsoft Azure, you will learn about data science and how to get started utilizing it in Microsoft Azure. First, you will learn the data science and the tools surrounding it. Next, you will discover how to create a development environment in Microsoft Azure. Finally, you will explore how to maintain and utilize that development environment. When you are finished with this course, you will have the skills and knowledge of data science to build your first data science project in Microsoft Azure. Software required: Microsoft Azure

Table of contents
  1. Course Overview
  2. Determining Which Tools to Use
  3. Setting up a Development Environment
  4. Using the New Development Environment

Set Up an Azure Machine Learning Workspace and Build Models

This section teaches you how to source, clean, and shape your data for further analysis in Microsoft Azure. You will learn how to build, train and evaluate models using Microsoft Azure.

Experimental Design for Data Analysis

by Janani Ravi

Jun 20, 2019 / 2h 45m

2h 45m

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Description

Providing crisp, clear, actionable points-of-view to senior executives is becoming an increasingly important role of data scientists and data professionals these days. Now, a point-of-view must represent a hypothesis, ideally backed by data. In this course, Experimental Design for Data Analysis, you will gain the ability to construct such hypotheses from data and use rigorous frameworks to test whether they hold true. First, you will learn how inferential statistics and hypothesis testing form the basis of data modeling and machine learning. Next, you will discover how the process of building machine learning models is akin to that of designing an experiment and how training and validation techniques help rigorously evaluate the results of such experiments. Then, you will round out the course by studying various forms of cross-validation, including both singular and iterative techniques to cope with independent, identically distributed data and grouped data. Finally, you will also learn how you can refine your models using these techniques with hyperparameter tuning. When you’re finished with this course, you will have the skills and knowledge to build and evaluate models, specifically including machine learning models, using rigorous cross-validation frameworks and hyperparameter tuning.

Table of contents
  1. Course Overview
  2. Designing an Experiment for Data Analysis
  3. Building and Training a Machine Learning Model
  4. Understanding and Overcoming Common Problems in Data Modeling
  5. Leveraging Different Validation Strategies in Data Modeling
  6. Tuning Hyperparameters Using Cross Validation Scores

Deploying and Managing Models in Microsoft Azure

by Jared Rhodes

Sep 2, 2020 / 59m

59m

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Description

One of the most overlooked processes in data science is managing the life cycle of models. In this course, Deploying and Managing Models in Microsoft Azure, you'll gain foundational knowledge of Azure Machine Learning. First, you'll discover how to create and utilize Azure Machine Learning. Next, you'll find out how to integrate with Azure DevOps. Finally, you'll explore how to utilize them together to automate the deployment and management of models. When you're finished with this course, you'll have the skills and knowledge of model life cycle management needed to manage a machine learning project. Software required: Microsoft Azure.

Table of contents
  1. Course Overview
  2. Deploying a Machine Learning Model
  3. Using Continuous Integration and Continuous Deployment
  4. Managing a Model's Lifecycle

Build Machine Learning Models with Azure Machine Learning Designer

by Ifedayo Bamikole

Feb 3, 2021 / 1h 9m

1h 9m

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Description

Creating Machine Learning Projects can be a bit tasking due to the high barrier of understanding Machine Learning languages and ability to debug when issue arise. In this course, Building Machine Learning Models with Azure Machine Learning Designer, you’ll learn to use drag and drop features to create an end-to-end machine learning pipeline for your ML Models. First, you’ll explore how to setup your ML pipeline in the Azure Machine Learning Studio. Then, you’ll learn how to ingest and transform different datasets formats. Finally, you’ll learn how to create models and make predictions in preparation to deploy the model. When you’re finished with this course, you’ll have the skills and knowledge of Building Models with Azure Machine Learning Designer needed to create effective Machine Learning Models.

Table of contents
  1. Course Overview
  2. Setting up a Pipeline with Azure Machine Learning Designer
  3. Ingest Data in a Designer Pipeline
  4. Define a Pipeline Data Flow with Designer Modules
  5. Applying Machine Learning Algorithms
  6. Publish Machine Learning Models in Azure Machine Learning Designer

Model Optimization and Management

Finally, this section of the skill addresses operational aspects of your data models, such as deploying them for use in Microsoft Azure, monitoring and evaluating their effectiveness, and communicating their insights back to the business.

Build Optimal Models with Azure Automated ML

by Axel Sirota

Feb 9, 2021 / 1h 15m

1h 15m

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Description

Building Machine Learning models means iterating multiple times over the best features, the best architectures, the best algorithms, until you get a model that serves your business purpose. In this course, Build Optimal Models with Azure Automated ML, you’ll learn to create the best machine learning model for your own specific data in just a few clicks. First, you’ll explore what Auto ML really is. Next, you’ll discover how to create optimal models both from the UI as well as the SDK. Finally, you’ll learn how to improve our models with more advanced techniques Azure ML offers. When you’re finished with this course, you’ll have the skills and knowledge of Automated Machine Learning needed to build the best Machine Learning model for your data in just a few clicks!

Table of contents
  1. Course Overview
  2. Introducing Azure Automated Machine Learning
  3. Creating Optimized Models with Azure Machine Learning Studio
  4. Creating Optimized Models with Azure Machine Learning SDK
  5. Final Thoughts

Create and Publish Pipelines for Batch Inferencing with Azure

by Kishan Iyer

Apr 22, 2021 / 2h 44m

2h 44m

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Description

A machine learning model goes through a number of stages in its lifecycle; from training, to evaluation, through deployment and then maintenance. While there are a number of tools available for these stages, their management can become overwhelming even for the seasoned ML engineer.

In this course, Create and Publish Pipelines for Batch Inferencing with Azure, you'll experience an intuitive and easy-to-maintain environment for all things ML and focus on building and running pipelines for batch inferences:

  1. discover the Azure ML service and the breadth of features it has to offer when it comes to building and managing ML models
  2. explore a number of data transformations which can be applied to a dataset by simply dragging and dropping various modules into the pipeline
  3. see that the handling of missing values, the standardization of numeric features as well as one-hot encoding for categorical fields can all be accomplished without writing a line of code
  4. use the pipeline to make predictions on new data
Once you have finished this course, you will have a clear understanding of the capabilities of Azure ML and specifically its designer when it comes to defining and managing pipelines - which can be used for both training and inferencing.

Table of contents
  1. Course Overview
  2. Getting Started with the Azure Machine Learning Designer
  3. Building a Model Training Pipeline
  4. Publishing a Batch Inference Pipeline
  5. Deploying a Batch Inference Pipeline
Learning Paths

Microsoft Exam DP-100 : Designing and Implementing a Data Science Solution on Azure

  • Number of Courses6 courses
  • Duration10 hours

Microsoft Azure offers a set of related services to address the day-to-day workflow of a data scientist. This skill teaches how these Azure services work together to enable various parts of this workflow.This path is designed to address the Microsoft DP-100 certification exam.

Courses in this path

Getting Started with Machine Learning using Azure

The courses in this section teach you how to get started with creating a machine learning workspace and to build a data pipeline . In addition, this part of the path discusses the role of effective communication as part of the data science workflow.

Set Up an Azure Machine Learning Workspace and Build Models

This section teaches you how to source, clean, and shape your data for further analysis in Microsoft Azure. You will learn how to build, train and evaluate models using Microsoft Azure.

Model Optimization and Management

Finally, this section of the skill addresses operational aspects of your data models, such as deploying them for use in Microsoft Azure, monitoring and evaluating their effectiveness, and communicating their insights back to the business.

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