Designing and Implementing a Data Science Solution on Azure

Schedule

Start	End	Duration	Location	Details

Course Details

Designing and Implementing a Data Science Solution on Azure

Course code: DP100

Duration: 3 Days

Prerequisite:

•   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.

Course Description:

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.

Intended Audience:

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.

Course Outlines:

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