Build automated and highly-scalable end-to-end Machine Learning models and pipelines in Azure using Tensorflow, Spark, and Kubernetes. About This Book * Implement highly-scalable end-to-end Machine Learning pipelines on Azure * Train and optimize advanced Deep Learning models on Spark using Azure Databricks * Deploy Machine Learning models for batch and real-time scoring using Azure Kubernetes Services Who This Book Is For If you are a data professional (data analyst, data engineer, data scientist, or machine learning developer) who wants to master scalable cloud-based Machine Learning architectures in Azure, this book is for you! Basics of Python and working knowledge of machine learning is required. What You Will Learn * Load and preprocess large datasets using Azure Data Factory * Train Machine Learning models using Azure Databricks * Learn to deploy machine learning models on Azure Kubernetes Services * Learn how to use DSVMs and Notebooks for Plotting and Embedding * Normalize data, and fill missing values using Spark in Azure Databricks * Implement feature extraction with word embedding using Natural Language Processing * Implement a distributed model training using Uber's Horovod Estimator * Use the Catalyst optimizer to improve query performance in Spark * Train Machine learning model using Azure ML Compute and Azure Databricks * Explore how to track and optimize the model performance over time * Implement a Real-time Scoring Service on Azure Kubernetes Services for automated Machine Learning deployments In Detail Data is eating the world and most data professionals need to make sense of it. The massive increase of data requires complex distributed systems, powerful algorithms and scalable cloud infrastructure in order to compute insights, train models and deploy them at scale. This book is a comprehensive guide to build large end-to-end Machine Learning pipelines in the cloud using Azure and Machine Learning services. Starting with Azure Data Science VMs, Notebooks and Azure Machine Learning Service you will perform and schedule common data loading and preparation technique using Azure Databricks and Azure Data Factory. Next, you will cover NLP, classical Machine Learning techniques such as ensemble techniques, time-series forecasting as well as Deep Learning for classification and regression. Leveraging state-of-the-art technologies, you will learn how to train, optimize and tune models using Automated-ML and Hyperdrive. You will learn to perform distributed training using Azure ML Compute. Later, you will learn different monitoring and optimization techniques in order to measure training performance in Spark using Azure Databricks. Finally, you will learn to deploy models to Kubernetes using Azure Machine Learning Service By the end of this book, you will master Azure Machine Learning Service and be able to build, optimize and operate scalable Machine Learning pipelines in Azure.

Book Information
ISBN 9781789807554
Author Christoph Korner
Format Paperback
Page Count 490
Imprint Packt Publishing Limited
Publisher Packt Publishing Limited