Designing and Implementing a Data Science Solution on Azure (DP-100T01)
Training plan
Module 1: Introduction to the Azure Machine Learning SDK
- Azure Machine Learning workspaces
- Azure Machine Learning tools and interfaces
- Azure Machine Learning experiments
Module 2: Use Automated Machine Learning in Azure Machine Learning
- What is machine learning?
- What is Azure Machine Learning studio?
- What is Azure Automated Machine Learning?
- Understand the AutoML process
Module 3: Create a classification model with Azure Machine Learning designer
- Identify classification machine learning scenarios
- What is Azure Machine Learning?
- What is Azure Machine Learning designer?
- Understand steps for classification
Module 4: Train a machine learning model with Azure Machine Learning
- Run a training script
- Using script parameters
- Registering models
Module 5: Work with Data in Azure Machine Learning
- Introduction to datastores
- Use datastores
- Introduction to datasets
- Use datasets
Module 6: Work with Compute in Azure Machine Learning
- Introduction to environments
- Introduction to compute targets
- Create compute targets
- Use compute targets
Module 7: Orchestrate machine learning with pipelines
- Introduction to pipelines
- Pass data between pipeline steps
- Reuse pipeline steps
- Publish pipelines
- Use pipeline parameters
- Schedule pipelines
Module 8: Deploy real-time machine learning services with Azure Machine Learning
- Deploy a model as a real-time service
- Consume a real-time inferencing service
- Troubleshoot service deployment
Module 9: Deploy batch inference pipelines with Azure Machine Learning
- Creating a batch inference pipeline
- Publishing a batch inference pipeline
Module 10: Tune hyperparameters with Azure Machine Learning
- Defining a search space
- Configuring sampling
- Configuring early termination
- Running a hyperparameter tuning experiment
Module 11: Automate machine learning model selection with Azure Machine Learning
- Automated machine learning tasks and algorithms
- Preprocessing and featurization
- Running automated machine learning experiments
Module 12: Explore differential privacy
- Understand differential privacy
- Configure data privacy parameters
Module 13: Explain machine learning models with Azure Machine Learning
- Feature importance
- Using explainers
- Creating explanations
- Visualizing explanations
Module 14: Detect and mitigate unfairness in models with Azure Machine Learning
- Consider model fairness
- Analyze model fairness with Fairlearn
- Mitigate unfairness with Fairlearn
Module 15: Monitor data drift with Azure Machine Learning
- Creating a data drift monitor
- Scheduling alerts
Module 16: Monitor models with Azure Machine Learning
- Enable Application Insights
- Capture and view telemetry
Recommended Prerequisite Knowledge
Prior to attending this training, students should have:
- earned the Microsoft Certified: Azure Data Fundamentals (DP900) certification or equivalent knowledge.
- an understanding of data science, including data preparation, model training, and evaluating competing models to choose the best one.
- an understanding of data science with Python; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.
Credentials and certification
Exam features
- Code: DP-100
- Title: Designing and Implementing a Data Science Solution on Azure
- Duration: 120 minutes
- Number of Questions: 40 to 60
- Question Format: Multiple choice, multiple response, scenario-based
- Passing Score: 700 out of 1000
- Cost: $0 (included in your training)
Exam topics
- Design and prepare a machine learning solution
- Explore data and train models
- Prepare a model for deployment
- Deploy and retrain a model
Eccentrix Corner article/publication
Microsoft Certified: Azure Data Scientist Associate (DP-100) Training
The Microsoft Certified: Azure Data Scientist Associate (DP-100) training equips professionals with the skills needed to design and implement data science solutions using Microsoft Azure. This course focuses on building and deploying machine learning models, managing datasets, and using Azure Machine Learning to solve real-world problems.
Ideal for data scientists, developers, and IT professionals, this training prepares participants to pass the DP-100 certification exam and validate their expertise in creating impactful data science solutions.
Why Choose the Azure Data Scientist Associate Training?
Data science is at the forefront of innovation, driving decision-making and insights across industries. This training empowers participants to leverage Azure’s powerful tools to build, train, and deploy machine learning models. The Azure Data Scientist Associate certification demonstrates your ability to transform raw data into valuable insights using cutting-edge cloud technologies.
Through hands-on exercises and expert guidance, you will develop practical skills that can be directly applied to data science projects, making you an indispensable part of any data-driven organization.
Key Skills Developed in the Training
Prepare and manage datasets
Learn to clean, preprocess, and structure data for machine learning models using Azure tools.Train and evaluate machine learning models
Gain expertise in training models using Azure Machine Learning, hyperparameter tuning, and performance evaluation.Implement machine learning pipelines
Master the creation and optimization of pipelines to automate workflows and enhance scalability.Deploy machine learning models
Learn how to deploy trained models as web services, integrate them into applications, and monitor their performance.Optimize model performance
Use tools like Azure Machine Learning Designer to improve accuracy, efficiency, and scalability of your solutions.Secure and manage machine learning environments
Understand how to protect sensitive data, manage resources, and ensure compliance with Azure’s security best practices.
The Azure Data Scientist Associate training is delivered by Microsoft-certified instructors who provide expert guidance and real-world scenarios. Participants will engage in interactive exercises to develop and deploy machine learning models, ensuring they gain hands-on experience with Azure Machine Learning tools.
This course not only prepares you for the DP-100 certification exam but also equips you with practical skills to tackle real-world challenges in data science.
Who Should Attend?
This training is ideal for:
- Data scientists seeking to enhance their skills with Azure Machine Learning tools
- Developers interested in integrating machine learning models into applications
- IT professionals preparing for the Azure Data Scientist Associate (DP-100) certification
- Organizations looking to leverage advanced data science solutions to improve decision-making
Advance Your Career with Azure Data Science Expertise
The Microsoft Certified: Azure Data Scientist Associate (DP-100) training provides you with the skills to design and implement advanced data science solutions in Azure. Enroll today to earn a globally recognized certification and take your data science career to the next level.
Frequently asked questions about the Microsoft Scientist Associate DP-100 training (FAQ)
What topics are covered in the DP-100 training?
The course includes dataset preparation, machine learning model training, pipeline implementation, deployment, and performance optimization.
Who is this course designed for?
This course is designed for data scientists, developers, and IT professionals working with machine learning and data science solutions.
What tools and services are used in this training?
Participants will use Azure Machine Learning, Azure Data Factory, and related services to manage and implement machine learning solutions.
What are the prerequisites for this course?
A basic understanding of programming (Python preferred), data analysis, and Azure fundamentals is recommended.
How does this training prepare for the DP-100 certification?
The course aligns with the exam objectives, combining theoretical knowledge and practical exercises to ensure success.
Does this training include hands-on projects?
Yes, participants will engage in hands-on projects to apply their skills to real-world scenarios.
