DP-100: Designing and Implementing a Data Science Solutions on Azure
Overview
The DP-100 certification course is designed for data scientists and machine learning engineers
who want to build and operationalize end-to-end ML solutions using Microsoft Azure. This
course provides deep hands-on exposure to Azure Machine Learning (Azure ML) — a powerful,
scalable platform for training, deploying, and managing machine learning models in the cloud.
Ideal for IT professionals, business decision-makers, and beginners in the cybersecurity domain, this course bridges the knowledge gap between business needs and modern cloud-based security technologies.
Ideal for IT professionals, business decision-makers, and beginners in the cybersecurity domain, this course bridges the knowledge gap between business needs and modern cloud-based security technologies.
What You’ll Learn
By the end of this course, you will be able to:
- Plan and manage machine learning solutions on Azure
- Set up and use Azure Machine Learning workspace
- Run experiments and train models using automated ML and Python SDK
- Deploy and operationalize machine learning models as REST endpoints
- Monitor, secure, and manage models in production
- Understand responsible AI principles and governance
Prerequisites
- Intermediate knowledge of Python and basic machine learning concepts
- Familiarity with cloud fundamentals (e.g., Azure or AWS basics)
- Experience with data handling and basic model building is recommended
Course Content Outline
1. Plan and Manage an Azure Machine Learning Workspace
- Create and configure Azure ML workspace
- Manage data storage and compute targets
- Use Azure ML Studio and Python SDK
- Understand data versioning and environment tracking
2. Prepare and Manage Data for Machine Learning
- Connect to and ingest structured and unstructured data
- Create and manage datastores and datasets
- Explore and transform data using notebooks
- Perform feature engineering and data cleansing
3. Perform Machine Learning in Azure
- Use AutoML and Designer for model training
- Train models with Python SDK and ML pipelines
- Tune hyperparameters using sweep configurations
- Evaluate and interpret model performance
4. Deploy and Operationalize Machine Learning Solutions
- Register, deploy, and manage models
- Create and consume real-time and batch endpoints
- Implement model versioning and CI/CD
- Monitor model performance, data drift, and retraining needs