Mastering Azure Machine Learning

By : Christoph Körner, Kaijisse Waaijer

Mastering Azure Machine Learning

By: Christoph Körner, Kaijisse Waaijer

Overview of this book

The increase being seen in data volume today requires distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy machine learning (ML) models. This book will help you improve your knowledge of building ML models using Azure and end-to-end ML pipelines on the cloud. The book starts with an overview of an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. It then focuses on Azure Machine Learning and takes you through the process of data experimentation, data preparation, and feature engineering using Azure Machine Learning and Python. You'll learn advanced feature extraction techniques using natural language processing (NLP), classical ML techniques, and the secrets of both a great recommendation engine and a performant computer vision model using deep learning methods. You'll also explore how to train, optimize, and tune models using Azure Automated Machine Learning and HyperDrive, and perform distributed training on Azure. Then, you'll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure Machine Learning, along with the basics of MLOps—DevOps for ML to automate your ML process as CI/CD pipeline. By the end of this book, you'll have mastered Azure Machine Learning and be able to confidently design, build and operate scalable ML pipelines in Azure.

Preface

About Mastering Azure Machine Learning

Section 1: Azure Machine Learning

Free Chapter

1. Building an end-to-end machine learning pipeline in Azure

Performing descriptive data exploration

Exploring common techniques for data preparation

Choosing the right ML model to train data

Optimization techniques

Deploying and operating models

Summary

2. Choosing a machine learning service in Azure

Demystifying the different Azure services for ML

Azure Cognitive Services and Custom Vision

Azure Machine Learning with GUIs

Azure Machine Learning workspace

Summary

Section 2: Experimentation and Data Preparation

3. Data experimentation and visualization using Azure

Preparing your Azure Machine Learning workspace

Visualizing high-dimensional data

Summary

4. ETL, data preparation, and feature extraction

Managing data and datasets in the cloud

Preprocessing and feature engineering with Azure Machine Learning DataPrep

Summary

5. Azure Machine Learning pipelines

Benefits of pipelines for ML workflows

Building and publishing an ML pipeline

Integrating pipelines with other Azure services

Summary

6. Advanced feature extraction with NLP

Understanding categorical data

Building a simple bag-of-words model

Leveraging term importance and semantics

Implementing end-to-end language models

Summary

Section 3: Training Machine Learning Models

7. Building ML models using Azure Machine Learning

Working with tree-based ensemble classifiers

Training an ensemble classifier model using LightGBM

Summary

8. Training deep neural networks on Azure

Introduction to deep learning

Training a CNN for image classification

Summary

9. Hyperparameter tuning and Automated Machine Learning

Hyperparameter tuning to find the optimal parameters

Finding the optimal model with Azure Automated Machine Learning

Summary

10. Distributed machine learning on Azure

Exploring methods for distributed ML

Using distributed ML in Azure

Summary

11. Building a recommendation engine in Azure

Introduction to recommender engines

Content-based recommendations

Collaborative filtering—a rating-based recommendation engine

Combining content and ratings in hybrid recommendation engines

Automatic optimization through reinforcement learning

Summary

Section 4: Optimization and Deployment of Machine Learning Models

12. Deploying and operating machine learning models

Deploying ML models in Azure

Building a real-time scoring service

Implementing a batch scoring pipeline

Inference optimizations and alternative deployment targets

Monitoring Azure Machine Learning deployments

Summary

13. MLOps—DevOps for machine learning

Ensuring reproducible builds and deployments

Validating your code, data, and models

Summary

14. What's next?

Understanding the importance of data

The future of ML is automated

Change is the only constant – preparing for change

Focusing first on infrastructure and monitoring

Controlled rollouts and A/B testing

Summary

Index

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Hyperparameter tuning to find the optimal parameters

In machine learning, we typically deal with parametric or non-parametric models. These models represent the distribution of the training data in order to make predictions for unseen data from the same distribution. While parametric models (such as linear regression, logistic regression, and neural networks) represent the training data distribution by using a learned set of parameters, non-parametric models describe the training data through other traits such as decision trees (all tree-based classifiers), training samples (k- nearest neighbors), or weighted training samples (support vector machine).

The Figure 9.1 outlines a few of the key differences between parametric and non- parametric models:

Figure 9.1: The difference between parametric and non-parametric models

The term hyperparameter refers to all parameters that are used to configure and tune the training process of parametric or non...

Mastering Azure Machine Learning

By : Christoph Körner, Kaijisse Waaijer

Mastering Azure Machine Learning

By: Christoph Körner, Kaijisse Waaijer

Overview of this book

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Hyperparameter tuning to find the optimal parameters

Figure 9.1: The difference between parametric and non-parametric models