DP-100 Azure Data Scientist Training
A practical, instructor-led training program for professionals who want to build, train, evaluate, deploy, and manage machine learning solutions using Microsoft Azure.
Course Overview
This course focuses on the core skills required to implement data science and machine learning solutions on Azure. Participants learn how to work with Azure Machine Learning, manage datasets, create experiments, train models, evaluate performance, and deploy machine learning solutions.
The training combines conceptual explanations, live demonstrations, guided labs, and practical machine learning workflows. It is suitable for professionals preparing for the DP-100 certification exam as well as teams building AI and machine learning solutions in the cloud.
What You Will Learn
- Design and implement machine learning solutions on Microsoft Azure
- Use Azure Machine Learning workspace, compute, datasets, and environments
- Prepare data for model training and experimentation
- Train, evaluate, compare, and register machine learning models
- Use automated machine learning and responsible AI capabilities
- Deploy models as online endpoints and manage inference workflows
- Prepare for Microsoft DP-100 certification topics through practical examples
Target Audience
- Data scientists
- Machine learning engineers
- AI engineers
- Data analysts moving into machine learning roles
- Developers building AI-enabled applications
- IT teams implementing Azure-based machine learning solutions
Prerequisites
- Basic understanding of machine learning concepts
- General knowledge of Python is recommended
- Basic familiarity with data preparation and analysis is helpful
- No advanced Azure experience is required, but basic Azure familiarity is beneficial
Detailed Syllabus
- Introduction to Azure Machine Learning and cloud-based data science workflows
- Azure Machine Learning workspace architecture and project organization
- Compute targets, compute instances, and compute clusters
- Working with datasets, data assets, and data preparation workflows
- Creating and managing machine learning environments
- Running experiments and tracking metrics
- Training machine learning models with scripts and notebooks
- Model evaluation, comparison, registration, and versioning
- Automated machine learning concepts and use cases
- Hyperparameter tuning and model optimization concepts
- Responsible AI, model interpretability, and fairness considerations
- Deploying models to managed online endpoints
- Monitoring deployed models and managing inference lifecycle
- End-to-end project scenario: prepare data, train a model, deploy, and test inference
Schedule
Next public session: June 17-20, 2026
Time: 09:00 AM - 05:00 PM Eastern Time
Format: Live online instructor-led training
Corporate and private group sessions can also be scheduled upon request.
Delivery and Materials
The course is delivered live online. Access details, meeting links, preparation instructions, and any required materials are sent by email after successful registration and payment.
Course content is provided for registered participants only. Unauthorized recording, redistribution, or sharing of training sessions or materials is not permitted.