DP-100: Designing and Implementing a Data Science Solution on Azure

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.

This course prepares you for the certification exam DP-100: Designing and Implementing a Data Science Solution on Azure to obtain the Microsoft Certified: Azure Data Scientist Associate Certificate. 

• Familiar with the basics of Azure and have experience in programming.
• Experience with the Python language as well as the Numpy, Pandas and Matplotlib libraries.
• Understanding the basics of Data Science, i.e., data preparation, storage and how models are trained using public libraries such as Scikit-Learn, PyTorch and Tensorflow.

Module 1: Introduction to Azure Machine Learning

• Getting Started with Azure Machine Learning
• Azure Machine Learning Tools

Module 2: No-Code Machine Learning with Designer

• Training Models with Designer
• Publishing Models with Designer

Module 3: Running Experiments and Training Models

• Introduction to Experiments
• Training and Registering Models

Module 4: Working with Data 

• Working with Datastores
• Working with Datasets

Module 5: Compute Contexts 

• Working with Environments
• Working with Compute Targe

Module 6: Orchestrating Operations with Pipelines

• Introduction to Pipelines
• Publishing and Running Pipelines

Module 7: Deploying and Consuming Models 

• Real-time Inferencing
• Batch Inferencing

Module 8: Training Optimal Models

• Hyperparameter Tuning
• Automated Machine Learning

Module 9: Interpreting Models

• Introduction to Model Interpretation
• Using Model Explainers

Module 10: Monitoring Models

• Monitoring Models with Application Insights
• Monitoring Data Drift