Practical Automated Machine Learning on Azure 1st Edition by Deepak Mukunthu, Parashar Shah, Wee Hyong Tok ISBN 9781492055594 149205559X

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Product details:

ISBN 10: 149205559X
ISBN 13: 9781492055594
Author: Deepak Mukunthu, Parashar Shah, Wee Hyong Tok

Practical Automated Machine Learning on Azure 1st Edition Table of contents:

I. Automated Machine Learning

1. Machine Learning: Overview and Best Practices

Machine Learning: A Quick Refresher

Model Parameters

Hyperparameters

Best Practices for Machine Learning Projects

Understand the Decision Process

Establish Performance Metrics

Focus on Transparency to Gain Trust

Embrace Experimentation

Don’t Operate in a Silo

An Iterative and Time-Consuming Process

Feature Engineering

Algorithm Selection

Hyperparameter Tuning

The End-to-End Process

Growing Demand

Conclusion

2. How Automated Machine Learning Works

What Is Automated Machine Learning?

Understanding Data

Detecting Tasks

Choosing Evaluation Metrics

Feature Engineering

Selecting a Model

Monitoring and Retraining

Bringing It All Together

Automated ML

How Automated ML Works

Preserving Privacy

Enabling Transparency

Guardrails

End-to-End Model Life-Cycle Management

Conclusion

II. Automated ML on Azure

3. Getting Started with Microsoft Azure Machine Learning and Automated ML

The Machine Learning Process

Collaboration and Monitoring

Deployment

Setting Up an Azure Machine Learning Workspace for Automated ML

Azure Notebooks

Notebook VM

Conclusion

4. Feature Engineering and Automated Machine Learning

Data Preprocessing Methods Available in Automated ML

Auto-Featurization for Automated ML

Auto-Featurization for Classification and Regression

Auto-Featurization for Time-Series Forecasting

Conclusion

5. Deploying Automated Machine Learning Models

Deploying Models

Registering the Model

Creating the Container Image

Deploying the Model for Testing

Testing a Deployed Model

Deploying to AKS

Swagger Documentation for the Web Service

Debugging a Deployment

Web Service Deployment Fails

Conclusion

6. Classification and Regression

What Is Classification and Regression?

Classification and Regression Algorithms

Using Automated ML for Classification and Regression

Conclusion

III. How Enterprises Are Using Automated Machine Learning

7. Model Interpretability and Transparency with Automated ML

Model Interpretability

Model Interpretability with Azure Machine Learning

Model Transparency

Understanding the Automated ML Model Pipelines

Guardrails

Conclusion

8. Automated ML for Developers

Azure Databricks and Apache Spark

ML.NET

SQL Server

Conclusion

9. Automated ML for Everyone

Azure Portal UI

Power BI

Preparing the Data

Automated ML Training

Understanding the Best Model

Understanding the Automated ML Training Process

Model Deployment and Inferencing

Enabling Collaboration

Azure Machine Learning to Power BI

Power BI Automated ML to Azure Machine Learning

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Tags: Deepak Mukunthu, Automated Machine, Azure, Parashar Shah, Wee Hyong Tok