Course Info
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.
What Will I Learn From This Course?
manage data ingestion and preparation
model training and deployment
machine learning solution monitoring in Microsoft Azure
Pre-requisite
Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques.
Specifically:
– Creating cloud resources in Microsoft Azure.
– Using Python to explore and visualize data.
– Training and validating machine learning models using common frameworks like Scikit
– Learn, PyTorch, and TensorFlow.
– Working with containers
Methodology
Lectures, visual presentations, hands-on demo files and lab exercises, Q&A.
Target 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 Outline for This Programme
Module 1: Getting Started with Azure Machine Learning
• Introduction to Azure Machine Learning
• Working with Azure Machine Learning
• Lab : Create an Azure Machine Learning Workspace
Module 2: Visual Tools for Machine Learning
• Automated Machine Learning
• Azure Machine Learning Designer
• Lab: Use Automated Machine Learning
• Lab: Use Azure Machine Learning Designer
Module 3: Running Experiments and Training Models
• Introduction to Experiments
• Training and Registering Models
• Lab: Run Experiments
• Lab: Train Models
Module 4: Working with Data
• Working with Datastores
• Working with Datasets
• Lab: Work with Data
Module 5: Working with Compute
• Working with Environments
• Working with Compute Targets
• Lab: Work with Compute
Module 6: Orchestrating Operations with Pipelines
• Introduction to Pipelines
• Publishing and Running Pipelines
• Lab: Create a Pipeline
Module 7: Deploying and Consuming Models
• Real-time Inferencing
• Batch Inferencing
• Continuous Integration and Delivery
• Lab: Create a Real-time Inferencing Service
• Lab: Create a Batch Inferencing Service
Module 8: Training Optimal Models
• Hyperparameter Tuning
• Automated Machine Learning
• Lab: Tune Hyperparameters
• Lab: Use Automated Machine Learning from the SDK
Module 9: Responsible Machine Learning
• Differential Privacy
• Model Interpretability
• Fairness
• Lab: Explore Differential provacy
• Lab: Interpret Models
• Lab: Detect and Mitigate Unfairness
Module 10: Monitoring Models
• Monitoring Models with Application Insights
• Monitoring Data Drift
• Lab: Monitor a Model with Application Insights
• Lab: Monitor Data Drift
