DP-100 Designing and
Implementing a
Data Science
Overview
Course Objective
Upon attending the DP-100: Designing and Implementing a Data Science Solution on Azure course, participants will be able to:
Provision and manage an Azure Machine Learning workspace, utilizing tools like Visual Studio Code, Jupyter Notebooks, and the Azure Machine Learning SDK for model development.
Build, train, and deploy machine learning models using both no-code solutions (Automated Machine Learning and Azure Machine Learning Designer) and code-based experiments.
Scale and optimize machine learning workflows by orchestrating experiments as pipelines, leveraging cloud compute resources, and tuning models for improved performance.
Implement responsible machine learning practices, ensuring model fairness, privacy, and transparency, while effectively monitoring deployed models and detecting data drift.
Who Should Attend
This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.
Prerequisites
Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques.
Specifically:
• Creating cloud resources in Microsoft Azure.
• Using Python to explore and visualize data.
• Training and validating machine learning models using common frameworks like ScikitLearn, PyTorch, and TensorFlow.
• Working with containers
To gain these prerequisite skills, take the following free online training before attending the course:
• Explore Microsoft cloud concepts.
• Create machine learning models.
• Administer containers in Azure
If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.
Training Calendar
Intake
Duration
Program Fees
Module
Module 1 - Getting Started with Azure Machine Learning
In this module, you will learn how to provision an Azure Machine Learning
workspace and use it to manage machine learning assets such as data, compute,
model training code, logged metrics, and trained models. You will learn how to
use the web-based Azure Machine Learning studio interface as well as the Azure
Machine Learning SDK and developer tools like Visual Studio Code and Jupyter
Notebooks to work with the assets in your workspace.
• Introduction to Azure Machine Learning
• Working with Azure Machine Learning
Lab: Create an Azure Machine Learning Workspace
After completing this module, you will be able to
• Provision an Azure Machine Learning workspace
• Use tools and code to work with Azure Machine Learning
Module 2 - No-Code Machine Learning
This module introduces the Automated Machine Learning and Designer visual
tools, which you can use to train, evaluate, and deploy machine learning models
without writing any code.
• Automated Machine Learning
• Azure Machine Learning Designer
Lab: Use Automated Machine Learning
Lab: Use Azure Machine Learning Designer
After completing this module, you will be able to
• Use automated machine learning to train a machine learning model
• Use Azure Machine Learning designer to train a model
• Storage, Azure Disk Storage, and Azure File Storage.
Module 3 - Running Experiments and Training Models
In this module, you will get started with experiments that encapsulate data
processing and model training code, and use them to train machine learning
models.
• Introduction to Experiments
• Training and Registering Models
Lab: Run Experiments
Lab: Train Models
After completing this module, you will be able to
• Run code-based experiments in an Azure Machine
• Learning workspace
• Train and register machine learning models
Module 4 - Working with Data
Data is a fundamental element in any machine learning workload, so in this
module, you will learn how to create and manage datastores and datasets in an
Azure Machine Learning workspace, and how to use them in model training
experiments.
• Working with Datastores
• Working with Datasets
Lab: Work with Data
After completing this module, you will be able to
• Create and use datastores
• Create and use datasets
Module 5 - Working with Compute
One of the key benefits of the cloud is the ability to leverage compute resources
on demand and use them to scale machine learning processes to an extent that
would be infeasible on your own hardware. In this module, you’ll learn how to
manage experiment environments that ensure consistent runtime consistency
for experiments, and how to create and use compute targets for experiment
runs.
• Working with Environments
• Working with Compute Targets
Lab: Work with Compute
After completing this module, you will be able to
• Create and use environments
• Create and use compute targets
Module 6 - Orchestrating Operations with Pipelines
Now that you understand the basics of running workloads as experiments that
leverage data assets and compute resources, it’s time to learn how to
orchestrate these workloads as pipelines of connected steps. Pipelines are key
to implementing an effective Machine Learning Operationalization (ML Ops)
solution in Azure, so you’ll explore how to define and run them in this module.
• Introduction to Pipelines
• Publishing and Running Pipelines
Lab: Create a Pipeline
After completing this module, you will be able to
• Flows
• Create pipelines to automate machine learning work
• Publish and run pipeline services
Module 7 - Deploying and Consuming Models
Models are designed to help decision making through predictions, so they’re
only useful when deployed and available for an application to consume. In this
module learn how to deploy models for real-time inferencing, and for batch
inferencing.
• Real-time Inferencing
• Batch Inferencing
• Continuous Integration and Delivery
Lab: Create a Real-time Inferencing Service
Lab: Create a Batch Inferencing Service
After completing this module, you will be able to
• Publish a model as a real-time inference service
• Publish a model as a batch inference service
• Describe techniques to implement continuous integration and delivery
Module 8 - Training Optimal Models
By this stage of the course, you’ve learned the end-to-end process for training,
deploying, and consuming machine learning models; but how do you ensure
your model produces the best predictive outputs for your data? In this module,
you’ll explore how you can use hyperparameter tuning and automated machine
learning to take advantage of cloud-scale compute and find the best model for
your data.
• Hyperparameter Tuning
• Automated Machine Learning
Lab: Tune Hyperparameters
Lab: Use Automated Machine Learning from the SDK
After completing this module, you will be able to
• Optimize hyperparameters for model training
• Use automated machine learning to find the optimal model for your
data
Module 9 - Responsible Machine Learning
Data scientists have a duty to ensure they analyze data and train machine
learning models responsibly, respecting individual privacy, mitigating bias, and
ensuring transparency. This module explores some considerations and
techniques for applying responsible machine learning principles.
• Differential Privacy
• Model Interpretability
• Fairness
Lab: Explore Differential provacy
Lab: Interpret Models
Lab: Detect and Mitigate Unfairness
After completing this module, you will be able to
• Apply differential provacy to data analysis
• Use explainers to interpret machine learning models
• Evaluate models for fairness
Module 10 - Responsible Machine Learning
After a model has been deployed, it’s important to understand how the model
is being used in production, and to detect any degradation in its effectiveness
due to data drift. This module describes techniques for monitoring models and
their data.
• Monitoring Models with Application Insights
• Monitoring Data Drift
Lab: Monitor a Model with Application Insights
Lab: Monitor Data Drift
After completing this module, you will be able to
• Use Application Insights to monitor a published model
• Monitor data drift
FAQs
General Questions:
Q: What is this course about?
This 4-day course provides in-depth training on how to design and implement data science solutions using Azure Machine Learning. Participants will learn how to manage data ingestion, prepare data, train and deploy models, and monitor machine learning solutions in the cloud. The course also covers using both code-based and no-code tools to build machine learning models, scale workflows, and implement best practices in responsible machine learning.
Q: Who should attend this course?
This course is designed for data scientists who already have a solid understanding of Python and machine learning frameworks like Scikit-Learn, PyTorch, and TensorFlow. It is ideal for professionals looking to build and manage machine learning solutions in Microsoft Azure, particularly those interested in leveraging cloud-scale capabilities for their models.
Q: What are the prerequisites for this course?
To succeed in this course, participants should have foundational knowledge in cloud computing, experience with data science tools and techniques, and familiarity with Python. Specifically, experience with creating cloud resources in Azure, training and validating machine learning models, and working with containers will be beneficial. Prior completion of basic Azure AI training is recommended for those new to machine learning.
Q: How long is the course?
The course is 4 days long, providing participants with comprehensive training on the Azure Machine Learning environment, including practical labs and exercises to apply what they learn in real-world scenarios.
Q: What key topics are covered in this course?
Introduction to Azure Machine Learning and workspace provisioning
No-code machine learning using Automated Machine Learning and Azure Designer
Running experiments and training models in Azure
Managing and working with data, compute resources, and machine learning environments
Orchestrating workflows with pipelines for effective ML Ops
Deploying models for real-time and batch inferencing
Hyperparameter tuning and automated machine learning to optimize model performance
Responsible machine learning practices including fairness, privacy, and transparency
Monitoring deployed models for performance and data drift
Q: Will I receive a certification after completing the course?
While this course provides essential skills for designing and implementing machine learning solutions on Azure, it does not directly lead to certification. However, it prepares participants for further specialized certifications such as the Azure AI Engineer Associate certification.
Program Content & Skills:
Q: What foundational Azure concepts will I strengthen in this course?
This course will strengthen your understanding of essential Azure Machine Learning concepts such as provisioning Azure Machine Learning workspaces, managing data and compute resources, training models, and deploying machine learning solutions. You will also reinforce your knowledge of orchestration through pipelines and gain insights into monitoring model performance and addressing challenges like data drift and model fairness.
Q: How does the course help me apply Azure knowledge to real-world scenarios?
Through hands-on labs, you will work on real-world scenarios like creating Azure Machine Learning workspaces, training and deploying models, managing data stores, and orchestrating machine learning workflows with pipelines. You’ll also gain practical experience in deploying real-time and batch inference services, applying hyperparameter tuning, and implementing responsible machine learning practices, equipping you to apply your Azure knowledge in production environments.
Q: What skills will I develop in implementing Azure services?
You’ll develop skills in building, training, and deploying machine learning models using Azure Machine Learning. Additionally, you’ll learn to create and manage data resources, leverage cloud compute for scalability, work with both no-code and code-based machine learning tools, and monitor deployed models for performance. These skills will enable you to effectively implement and manage end-to-end machine learning workflows on Azure.
Q: Will I learn how to work with different Azure management tools?
Yes, this course covers various Azure Machine Learning tools, including the Azure Machine Learning SDK, Visual Studio Code, Jupyter Notebooks, and the Azure Machine Learning studio interface. You’ll gain hands-on experience using these tools to manage machine learning assets, orchestrate experiments, deploy models, and monitor their performance, ensuring you can navigate and utilize the full Azure ecosystem.
Q: How does this course prepare me for using Azure professionally?
By providing practical experience in the end-to-end process of machine learning, from data management to model deployment and monitoring, this course prepares you to handle real-world challenges in professional settings. You will be equipped to design, implement, and manage scalable machine learning solutions, ensuring you’re ready to apply Azure’s capabilities in complex, production-ready scenarios.
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