What does an Azure data scientist do?

Microsoft Certified Azure Data Scientist Associates are subject matter experts who can plan and create a working environment for data science workloads on Microsoft Azure. They can run data experiments, train predictive models, and manage, optimize, and deploy machine learning models into production.

How do I become an Azure data scientist?

As an Azure data scientist aspirant, you have to undertake the DP-100 examination to get Microsoft certified as an Azure data scientist associate. The data scientist certification path is organized into three levels - Fundamentals, Associate, and Expert.

Can you get a job with Azure certification?

Depending on your role, earning an Azure certification can help you access more job opportunities that may require additional credentials. When you obtain a new Microsoft Azure certification and gain more working experience, you may be able to leverage your expertise for a higher salary.

Do certifications matter in data science?

Data science certifications give you an opportunity not only to develop skills that are hard to find in your desired industry but also to validate your data science know-how so that recruiters and hiring managers know what they're getting if they hire you.

DP-100T01: A Designing and Implementing a Data Science Solution on Azure

Instructor-led course to gain the skills needed to become a Microsoft Certified AI Engineer

What you will learn

Basics of Machine Learning
Basics of Data Science
Utilizing the Data Store
Working with datasets
Hands on Experience with Microsoft Azure
Running Experiments on Microsoft Azure
Utilizing no-Code Machine Learning
Modeling Interpretability in Azure ML
Orchestrating Machine Learning Workflows
Deploy and Consume Models
Training Optimal Models
Using Responsible AI

Requirements

Experience with Python
Experience with PyTorch
Experience with TensorFlow
Experience with Scikit Learn
Students taking this course need internet speeds of at least 10 Mbps
At least two monitors are recommended with 1920 x1080 resolution

Description

DP-100: Designing and Implementing a Data Science Solution on Azure is a Microsoft Certification course that helps you in understanding wide range of Machine Learning models with their application in Data Science on the Azure Cloud. The students will learn to train and utilize different models using Azure Machine Learning Studio to execute machine learning workloads in the Azure Cloud. It helps in using no code Machine Learning Models as well as developing Custom Responsible Machine Learning Models.

The DP-100 certification course begins with the Introduction of Azure and Azure Machine Learning. After providing basic knowledge it provides a hands on experience with no-code machine learning using the Visual Tools. Moreover it helps you in training models and running experiments. The Average Salary of certified Microsoft Azure Data Scientist Associate (DP-100) in United States is $122,802.

Data is essential in Machine Learning Paradigm so the DP100 course guides the process of using Datastore and consuming the Datasets. Once the datasets are managed, compute instances are created to run the codes on the Azure Cloud.

Now that instances are ready the pipelines are managed and different models are trained and consumed for Decision Making. Once the Models are ready the Model training process is Optimized.

By the end of DP100 training course utilization of Responsible Machine Learning and monitoring the models for better performance is discussed.

This course will help you become Microsoft Azure Data Scientist along with passing the Microsoft Certification exam.

The DP-100 roadmap comprises 10 modules; module 0 is the introductory module. In contrast, the rest of the modules are developed following the course requirements. Each module has 3-part lessons, Labs, and Quizzes.

So, if you are interested in deploying machine learning models or building machine learning solutions on the cloud, this exam DP 100 study guide is for you. Feel free to contact us anytime.

Prominent Features

Instructor-Led Course
Live Training Option
Flexible Deadlines
Self-Paced Learning
Shareable Certificate
Skillset Building To Get Job Ready
Scenario Based Labs
Earn a Career Credential
Licenses For Lab Completion
Enhanced Course Material
Helpdesk Support
Guidelines To Overcome Professional Challenges
Command Over Professional Expertise
Prepared Virtual Machines for Labs
Business Plan Option

Course Content

22 Lectures
10 Quizzes
20 Hands-on Labs

25 Hours
lessons and hands-on labs

Module 00: Course Introduction

This module consists of a brief introduction of the Instructor along with the overall course introduction giving a heads up to the students in context of the course contents and all modules information.

	Lessons	Duration
	Lesson 01: Introduction of Course	12:14

Module 01: Getting Started with Azure Machine Learning

This session will teach you how to set up an Azure Machine Learning workspace and use it to manage machine learning assets including data, computation, model training code. To interact with the assets in your workspace and utilize the web-based Azure Machine Learning studio interface, as well as the Azure Machine Learning SDK and development tools such as Visual Studio Code and Jupyter Notebooks.

	Lessons	Duration
	Lesson 01: Introduction to Azure Machine Learning	25:32

	Labs	Duration
	Lab 1A: Create an Azure Machine Learning Workspace	32:04
	Lab 1B: Introduction to Azure ML SDK	32:31

Module 02: No Code Machine Learning

This module presents the Automated Machine Learning and Designer visual tools, which may be used to train, assess, and deploy machine learning models without writing any code.

	Lessons	Duration
	Lesson 01: Automated Azure Machine Learning	22:03
	Lesson 02:	22:31

	Labs	Duration
	Lab 2A: Use Automated Machine Learning	26:57
	Lab 2B: Deploying Inference Pipeline Using Azure ML Designer	42:28

Module 03: Running Experiments and Training Models

In this module, you will begin by creating experiments that contain data processing and model training code, which you will then use to train machine learning models.

	Lessons	Duration
	Lesson 01: Introduction to Experiments	18:06
	Lesson 02: Training and Registering Models	22:28

	Labs	Duration
	Lab 3A: Creating Experiment	35:54
	Lab 3B: Training Models	46:16

Module 04: Working with Data

Because data is an essential component of any machine learning workload, this module will teach you how to create and maintain datastores and datasets in an Azure Machine Learning workspace, as well as how to utilize them in model training trials.

	Lessons	Duration
	Lesson 01: Working with Datastores	16:27
	Lesson 02: Working with Datasets	25:51

	Labs	Duration
	Lab 4A: Working with Datastores	49:31
	Lab 4B: Working with Datasets	45:50

Module 05: Working With Compute

This module will teach you how to design and use compute targets for experiment runs, as well as how to manage experiment settings that provide constant runtime consistency for experiments.

	Lessons	Duration
	Lesson 01: Working with Environment	19:02
	Lesson 02: Working with Compute Targets	15:16

	Labs	Duration
	Lab 5A: Working with Environment	51:02
	Lab 5B: Working with Compute	47:58

Module 06: Orchestrating Machine Learning Workflows

Pipelines are essential for establishing a successful Machine Learning Operationalization (ML Ops) solution in Azure, thus this module will show you how to create and operate them

	Lessons	Duration
	Lesson 01: Introduction to Pipelines	22:26
	Lesson 02: Publishing and Running Pipelines	19:25

	Labs	Duration
	Lab 6A: Create a Pipeline	54:12
	Lab 6B: Publishing a Pipeline	34:59

Module 07: Deploying and Consuming Models

Models are intended to aid decision making through predictions, hence they are only helpful when installed and ready for consumption by an application. Learn how to deploy models for both real-time and batch inference in this module.

	Lessons	Duration
	Lesson 01: Real-Time Inferencing	21:54
	Lesson 02: Batch Inferencing	17:13

	Labs	Duration
	Lab 7A: Creating a Real-time inference Service	39:43
	Lab 7B: Creating a Batch inference Service	47:48

Module 08: Training Optimal Models

This module will show you how to use the Azure Machine Learning SDK to do hyperparameter tweaking and automated machine learning to identify the optimal model for your data.

	Lessons	Duration
	Lesson 01: Hyper Parameter Tuning	25:35
	Lesson 02: Using Automated Machine Learning	12:30

	Labs	Duration
	Lab 8A: Tuning Hyper Parameters	49:25
	Lab 8B: Using Automated Machine Learning from SDK	46:14

Module 09: Responsible Machine Learning

This session looks at some of the considerations and approaches for implementing responsible machine learning concepts.

	Lessons	Duration
	Lesson 01: Differential Privacy	17:40
	Lesson 02: Model Interpretability	17:50
	Lesson 03: Fairness	09:05

	Labs	Duration
	Lab 9A: Reviewing Automated Machine Learning explanation	32:11
	Lab 9B: Interpret Models	44:22

Module 10: Monitoring Models

After deploying a model, it is critical to evaluate how the model is being utilized in production and to detect any decline in its efficacy caused by data drift. This lesson explains how to monitor models and their data

	Lessons	Duration
	Lesson 01: Monitoring Models with Application Insights	10:06
	Lesson 02: Monitoring Data Drift	14:36

	Labs	Duration
	Lab 10A: Monitoring a Model	27:41
	Lab 10B: Monitoring Data Drift	25:04

About the Instructor

Muhammad Omer Aftab ( Azure Data Scientist )

Microsoft certified Azure Data Scientist Associate

Muhammad Omer Aftab is a Microsoft Certified Azure Data Scientist Associate. He holds a Bachelor of Science in Computer Sciences and a Master of Philosophy in Software Engineering. He is fascinated with Artificial Intelligence and its subfields, such as Machine Learning, Deep Learning, and Reinforcement Learning. He enjoys learning about it by devising practical applications of what he has learned. He believes that effort and passion, rather than information, is what motivates us to achieve our life's... Read More

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

Manage Azure resources for machine learning (25-30%)
Create an Azure Machine Learning workspace
- Create an Azure Machine Learning workspace
- Configure workspace settings
- Manage a workspace by using Azure Machine Learning studio
Manage data in an Azure Machine Learning workspace
- Select Azure storage resources
- Register and maintain datastores
- Create and manage datasets
Manage compute for experiments in Azure Machine Learning
- Determine the appropriate compute specifications for a training workload
- Create compute targets for experiments and training
- Configure Attached Compute resources including Azure Databricks
- Monitor compute utilization
Implement security and access control in Azure Machine Learning
- Determine access requirements and map requirements to built-in roles
- Create custom roles
- Manage role membership
- Manage credentials by using Azure Key Vault
Set up an Azure Machine Learning development environment
- Create compute instances
- Share compute instances
- Access Azure Machine Learning workspaces from other development environments
Set up an Azure Databricks workspace
- Create an Azure Databricks workspace
- Create an Azure Databricks cluster
- Create and run notebooks in Azure Databricks
- Link and Azure Databricks workspace to an Azure Machine Learning workspace
Run experiments and train models (20-25%)
Create models by using the Azure Machine Learning designer
- Create a training pipeline by using Azure Machine Learning designer
- Ingest data in a designer pipeline
- Use designer modules to define a pipeline data flow
- Use custom code modules in designer
Run model training scripts
- Create and run an experiment by using the Azure Machine Learning SDK
- Configure run settings for a script
- Consume data from a dataset in an experiment by using the Azure Machine Learning SDK
- Run a training script on Azure Databricks compute
- Run code to train a model in an Azure Databricks notebook
Generate metrics from an experiment run
- Log metrics from an experiment run
- Retrieve and view experiment outputs
- Use logs to troubleshoot experiment run errors
- Use MLflow to track experiments
- Track experiments running in Azure Databricks
Use Automated Machine Learning to create optimal models
- Use the Automated ML interface in Azure Machine Learning studio
- Use Automated ML from the Azure Machine Learning SDK
- Select pre-processing options
- Select the algorithms to be searched
- Define a primary metric
- Get data for an Automated ML run
- Retrieve the best model
Tune hyperparameters with Azure Machine Learning
- Select a sampling method
- Define the search space
- Define the primary metric
- Define early termination options
- Find the model that has optimal hyperparameter values
Deploy and operationalize machine learning solutions (35-40%)
Select compute for model deployment
- Consider security for deployed services
- Evaluate compute options for deployment
Deploy a model as a service
- Configure deployment settings
- Deploy a registered model
- Deploy a model trained in Azure Databricks to an Azure Machine Learning endpoint
- Consume a deployed service
- Troubleshoot deployment container issues
Manage models in Azure Machine Learning
- Register a trained model
- Monitor model usage
- Monitor data drift
Create an Azure Machine Learning pipeline for batch inferencing
- Configure a ParallelRunStep
- Configure compute for a batch inferencing pipeline
- Publish a batch inferencing pipeline
- Run a batch inferencing pipeline and obtain outputs
- Obtain outputs from a ParallelRunStep
Publish an Azure Machine Learning designer pipeline as a web service
- Create a target compute resource
- Configure an inference pipeline
- Consume a deployed endpoint
Implement pipelines by using the Azure Machine Learning SDK
- Create a pipeline
- Pass data between steps in a pipeline
- Run a pipeline
- Monitor pipeline runs
Apply ML Ops practices
- Trigger an Azure Machine Learning pipeline from Azure DevOps
- Automate model retraining based on new data additions or data changes
- Refactor notebooks into scripts
- Implement source control for scripts
Implement responsible machine learning (5-10%)
Use model explainers to interpret models
- Select a model interpreter
- Generate feature importance data
Describe fairness considerations for models
- Evaluate model fairness based on prediction disparity
- Mitigate model unfairness
Describe privacy considerations for data
- Describe principles of differential privacy
- Specify acceptable levels of noise in data and the effects on privacy

Frequently Asked Questions

Does the DP-100T01-A course have hands-on labs?

Yes, the DP-100T01 -A course has hands-on practical labs for all the modules to practice different Data Science processes in real lab environment; which are available 24 hours a day, for 30 days.
What are the working hours for support team?

Support team is available for assistance 8 hours a day (from Monday to Friday)
What is the meaning of ‘enhanced content’?

Instructor Brandon's 'Enhanced Content' offers an exclusively designed course increase in the official Microsoft Curriculum, adding real-world changes that resonate with today's software updates.
Is there any demo available?

Yes, demonstrations are available as reference, showing the steps to achieve solutions so that you do not get stuck during the hands-on component.
What is the refund policy for the course?

There is no refund granted once the course is purchased as we provide all the course contents upon purchasing. All completed purchases are final and non-refundable. For more details, click here.
How long do I have access to the course?

You have access to your purchased course for 2 months: 60 days for lectures and 30 days for labs.
Are there any special licenses being offered during training?

We provide our students with the licenses required to do the course and the labs.
How do I extend lab time beyond 30 days?

An ‘Extension Fee’ covers all the costs of your licenses and support, so for a fee of $400, you can extend your lab time.
How many hours should I spent each week on the course?

We suggest around 20 hours a week in order to complete this course on time.

DP-100T01: A Designing and Implementing a Data Science Solution on Azure

Q: Is Microsoft Certified Azure data scientist Associate worth IT?

Microsoft Data Science Certification is worth getting as it equips a data scientist with impressive cloud computing skills as they build on their knowledge of Python and Machine learning. The certificate validates their skills and proves their professionalism to hiring managers.

Instructor-led course to gain the skills needed to become a Microsoft Certified AI Engineer

Buy Now

Prominent Features

25 hours plus of course content
Virtual Machine for Labs
22 Lectures, 20 Labs and 10 Quizzes
Dedicated support (8 hours a day)
Course completion certification

DP-100T01: A Designing and Implementing a Data Science Solution on Azure

What you will learn

Requirements

Description

Prominent Features

About the Instructor

Muhammad Omer Aftab ( Azure Data Scientist )

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

Create an Azure Machine Learning workspace

Manage data in an Azure Machine Learning workspace

Manage compute for experiments in Azure Machine Learning

Implement security and access control in Azure Machine Learning

Set up an Azure Machine Learning development environment

Set up an Azure Databricks workspace

Create models by using the Azure Machine Learning designer

Run model training scripts

Generate metrics from an experiment run

Use Automated Machine Learning to create optimal models

Tune hyperparameters with Azure Machine Learning

Select compute for model deployment

Deploy a model as a service

Manage models in Azure Machine Learning

Create an Azure Machine Learning pipeline for batch inferencing

Publish an Azure Machine Learning designer pipeline as a web service

Implement pipelines by using the Azure Machine Learning SDK

Apply ML Ops practices

Use model explainers to interpret models

Describe fairness considerations for models

Describe privacy considerations for data

Frequently Asked Questions

DP-100T01: A Designing and Implementing a Data Science Solution on Azure

Prominent Features

If you have any questions contact us at response@instructorbrandon.com

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