DP-100T01: Designing And Implementing A Data Science Solution On Azure

The DP-100T01-A: Designing and Implementing a Data Science Solution on Azure course is built for data scientists who want to scale their machine learning operations in the cloud. Over three intensive days, learners will gain hands-on experience with Azure Machine Learning to manage the full ML lifecycle—from data ingestion and model training to deployment and monitoring.

Participants will start by creating an Azure Machine Learning workspace, using both the SDK and visual tools like Designer and AutoML. You'll learn how to structure experiments, manage compute resources, create reusable ML pipelines, and run hyperparameter tuning at scale.

The course emphasizes operationalizing models for both real-time and batch inferencing, integrating CI/CD practices for ML, and deploying services securely. Key skills include managing datastores and datasets, automating ML workflows with pipelines, and monitoring models with Application Insights and data drift detection.

Responsible AI practices are also a core part of the training. You’ll explore fairness, differential privacy, and model interpretability to ensure your AI solutions are ethical and compliant.

Designed for professionals already familiar with Python and ML frameworks like Scikit-Learn, PyTorch, or TensorFlow, this course prepares you for the Microsoft Certified: Azure Data Scientist Associate certification and equips you with the skills to operationalize AI projects that scale across your business.