Microsoft - Build and Operate Machine Learning Solutions with Azure
- Offered byCoursera
Build and Operate Machine Learning Solutions with Azure at Coursera Overview
Duration | 31 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Build and Operate Machine Learning Solutions with Azure at Coursera Highlights
- Flexible deadlines Reset deadlines in accordance to the schedule
- Earn a certificate upon completion from Coursera
Build and Operate Machine Learning Solutions with Azure at Coursera Course details
- Azure Machine Learning is a cloud platform for training, deploying, managing, and monitoring machine learning models. In this course, you will learn how to use the Azure Machine Learning Python SDK to create and manage enterprise-ready ML solutions
- This is the third course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurecertification exam
- The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning
- This specialization teaches students 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
- Each course teaches you the concepts and skills that are measured by the exam
Build and Operate Machine Learning Solutions with Azure at Coursera Curriculum
Use the Azure Machine Learning SDK to train a model
Introduction to Modern Data Warehouse Analytics in Azure
Lesson introduction
Azure Machine Learning workspaces
Azure Machine Learning tools and interfaces
Azure Machine Learning experiments
Lesson summary
Lesson Introduction
Lesson summary
Course Syllabus
How to be successful in this course
Exercise - Create a workspace
Exercise - Run experiments
Additional Reading
Run a training script
Using script parameters
Registering models
Exercise - Training and registering a model
Exercise quiz
Exercise quiz
Knowledge check
Exercise quiz
Knowledge check
Test prep
Work with Data and Compute in Azure Machine Learning
Lesson Introduction
Introduction to datastores
Use datastores
Lesson summary
Lesson introduction
Environments in Azure Machine Learning
Introduction to compute targets
Lesson summary
Introduction to datasets
Use datasets
Exercise - Work with data
Additional Reading
Creating environments
Create compute targets
Use compute targets
Exercise - Work with Compute Contexts
Additional reading
Exercise quiz
Knowledge check
Exercise quiz
Knowledge check
Test prep
Orchestrate pipelines and deploy real-time machine learning services with Azure Machine Learning
Lesson introduction
Introduction to pipelines
Pass data between pipeline steps
Lesson summary
Lesson Introduction
Troubleshoot service deployment
Lesson summary
OutputFileDatasetConfig Step Inputs and Outputs
Reuse pipeline steps
Publish pipelines
Use pipeline parameters
Schedule pipelines
Exercise - Create a pipeline
Additional Reading
Deploy a model as a real-time service
Consume a real-time inferencing service
Exercise - Deploy a model as a real-time service
Exercise quiz
Knowledge check
Exercise quiz
Knowledge check
Test prep
Deploy batch inference pipelines and tune hyperparameters with Azure Machine Learning
Lesson introduction
Lesson Summery
Lesson introduction
Defining a search space
Configuring early termination
Lesson summary
Creating a batch inference pipeline
Publishing a batch inference pipeline
Exercise - Create a batch inference pipeline
Configuring sampling
Running a hyperparameter tuning experiment
Exercise - Tune hyperparameters
Exercise quiz
Knowledge check
Exercise quiz
Knowledge check
Test prep
Select models and protect sensitive data
Lesson introduction
Automated machine learning tasks and algorithms
Preprocessing and featurization
Lesson summary
Lesson introduction
Understand differential privacy
Configure data privacy parameters
Lesson Summery
Lesson introduction
Feature importance
Using explainers
Visualizing explanations
Lesson summary
Running automated machine learning experiments
Exercise - Using automated machine learning
Additional Reading
Exercise - Use differential privacy
Additional Reading
Creating explanations
Exercise - Interpret models
Additional Reading
Exercise quiz
Knowledge check
Exercise quiz
Knowledge check
Exercise quiz
Knowledge check
Test prep
Monitor machine learning deployments
Lesson introduction
Consider model fairness
Analyze model fairness with Fairlearn
Mitigate unfairness with Fairlearn
Lesson summary
Lesson introduction
Enable Application Insights
Lesson summary
Lesson introduction
Creating a data drift monitor
Scheduling alerts
Lesson summary
Congratulations
Exercise - Use Fairlearn with Azure Machine Learning
Additional Reading
Capture and view telemetry
Exercise - Monitor a model
Additional Reading
Exercise - Monitor data drift
Additional Reading
Next steps
Exercise quiz
Knowledge check
Exercise quiz
Knowledge check
Exercise quiz
Knowledge check
Test prep
Build and Operate Machine Learning Solutions with Azure at Coursera Admission Process
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