Advanced Azure Machine Learning

Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes

Key Features

• Make sense of data on the cloud by implementing advanced analytics
• Train and optimize advanced deep learning models efficiently on Spark using Azure Databricks
• Deploy machine learning models for batch and real-time scoring with Azure Kubernetes Service (AKS)

Book Description

The increase in data needs distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy models. This book will help you improve your knowledge of building machine learning (ML) models using Azure and end-to-end ML pipelines on the cloud.

The book starts with Azure Data Science Virtual Machines, Azure Notebooks, and Azure Machine Learning services, and takes you through the data experimentation process. You’ll then perform and schedule data loading and preparation techniques using Azure Databricks and Azure Data Factory. You’ll learn feature extraction using natural language processing (NLP), classical machine learning techniques such as ensemble learning, and time-series forecasting using a range of deep learning methods. You’ll also explore how to train, optimize, and tune models using Azure AutoML and HyperDrive, and perform distributed training using Azure Machine Learning Compute (AmlCompute). Next, you’ll learn different monitoring and optimization techniques to measure training performance in Spark using Azure Databricks. Lastly, you’ll cover model deployment to Kubernetes with Azure Machine Learning services.

By the end of this book, you’ll have mastered machine learning development and be able to confidently build and operate scalable machine learning pipelines in Azure.

What you will learn

• Load and preprocess large datasets using Azure Data Factory
• Train machine learning models using Azure Databricks
• Deploy ML models in Azure Kubernetes Service
• Implement feature extraction with word embedding using NLP
• Employ distributed model training using Uber’s HorovodEstimator
• Improve query performance in Spark using the Catalyst optimizer
• Train ML models using AmlCompute and Azure Databricks
• Track and optimize deep learning model performance over time

Who This Book Is For

This machine learning book is for data professionals, data analysts, data engineers, data scientists, or machine learning developers who want to master scalable cloud-based machine learning architectures in Azure. This book will help you use advanced Azure services to build intelligent machine learning applications. A basic understanding of Python and working knowledge of machine learning are mandatory.