Overview
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 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.
What you’ll learn
Module 1: Getting Started with Azure Machine Learning
- Introduction to Azure Machine Learning
- Working with Azure Machine Learning
Module 2: Visual Tools for Machine Learning
- Automated Machine Learning
- Azure Machine Learning 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: Working with Compute
- 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
- Continuous Integration and Delivery
Module 8: Training Optimal Models
- Hyperparameter Tuning
- Automated Machine Learning
Module 9: Responsible Machine Learning
- Differential Privacy
- Model Interpretability
- Fairness
Module 10: Monitoring Models
- Monitoring Models with Application Insights
- Monitoring Data Drift
Accreditation
ITCE is a Certified Microsoft Learning Partner.
Calendar
Frequently Asked Questions
Who should attend?
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.
Are there any prerequisites for this course?
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
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