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Build and Operate Machine Learning Solutions with Azure

IT & Software

Self-paced course

Certification program

Machine Learning

Data Science

Coursera

From Nov 3, 2023

English

Beginner

Computer Science

Cloud Computing

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

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

Knowledge check

Test prep

1 week: Use the Azure Machine Learning SDK to train a model

Introduction to datastores

Use datastores

Environments in Azure Machine Learning

Introduction to compute targets

Introduction to datasets

Use datasets

Exercise - Work with data

Creating environments

Create compute targets

Use compute targets

Exercise - Work with Compute Contexts

Additional reading

2 week: Work with Data and Compute in Azure Machine Learning

Introduction to pipelines

Pass data between pipeline steps

Troubleshoot service deployment

OutputFileDatasetConfig Step Inputs and Outputs

Reuse pipeline steps

Publish pipelines

Use pipeline parameters

Schedule pipelines

Exercise - Create a pipeline

Deploy a model as a real-time service

Consume a real-time inferencing service

Exercise - Deploy a model as a real-time service

3 week: Orchestrate pipelines and deploy real-time machine learning services with Azure Machine Learning

Lesson Summery

Defining a search space

Configuring early termination

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

4 week: Deploy batch inference pipelines and tune hyperparameters with Azure Machine Learning

Automated machine learning tasks and algorithms

Preprocessing and featurization

Understand differential privacy

Configure data privacy parameters

Feature importance

Using explainers

Visualizing explanations

Running automated machine learning experiments

Exercise - Using automated machine learning

Exercise - Use differential privacy

Creating explanations

Exercise - Interpret models

5 week: Select models and protect sensitive data

Consider model fairness

Analyze model fairness with Fairlearn

Mitigate unfairness with Fairlearn

Enable Application Insights

Creating a data drift monitor

Scheduling alerts

Congratulations

Exercise - Use Fairlearn with Azure Machine Learning

Capture and view telemetry

Exercise - Monitor a model

Exercise - Monitor data drift

Next steps

6 week: Monitor machine learning deployments

Build and Operate Machine Learning Solutions with Azure

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Build and Operate Machine Learning Solutions with Azure