MS-DP-100T01 - Designing and Implementing a Data Science Solution on Azure

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

****This course is not Microsoft Software Assurance Training Voucher (SATV) eligible.****

****This course is not Microsoft Software Assurance Training Voucher (SATV) eligible.****
Audience profile
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.

Instructor did a great job, from experience this subject can be a bit dry to teach but he was able to keep it very engaging and made it much easier to focus. Student
Excellent presentation skills, subject matter knowledge, and command of the environment. Student
Instructor was outstanding. Knowledgeable, presented well, and class timing was perfect. Student

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Prerequisites


Before attending this course, students must have:
A fundamental knowledge of Microsoft Azure
Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib.
Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.

Detailed Class Syllabus


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 Targets

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