Mastering Machine Learning on AWS

Master machine learning techniques with AWS to create engaging applications using SageMaker, Apache Spark, EMR, and TensorFlow. About This Book * Build powerful machine learning applications on AWS using SageMaker, Apache Spark and TensorFlow * Learn model optimization, and how to scale your models using simple and secure APIs * Build, train, tune and deploy neural network models to accelerate model performance on the cloud Who This Book Is For This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud purely using AWS, and its integration services. Some understanding of machine learning concepts, Python programming and AWS is recommended. What You Will Learn * Manage artificial intelligence workflows using AWS cloud to deploy services that feed smart data products * Use SageMaker services to create recommendation models * Scale model training and deployment using Apache Spark on Elastic Map Reduce (EMR) * Explore how to cluster big data through EMR and seamlessly integrate with SageMaker * Build deep learning models on AWS through TensorFlow and deploy these as services * Combine Apache Spark and Amazon SageMaker to obtain the best of both technologies In Detail AWS is constantly driving new innovations that empower data scientists through the use of a vast number of machine learning cloud services. This book is a perfect reference to learn and implement advanced machine learning algorithms on AWS cloud. Throughout the book, we aim to introduce various practical machine learning algorithms and discuss how they can be trained, tuned and deployed in AWS using Apache Spark on EMR, SageMaker, and Tensorflow. For each algorithm covered (such as XGBoost, linear models, factorization machines, deep nets and more!) we provide an overview of the underlying theory as well as a detailed practical application that solves a real-world problem. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. You will learn to use SageMaker Notebooks and EMR Notebooks to carry out various tasks such as smart analytics, recommendation engines, predictive modeling, sentiment analysis and more. By the end of this book, you will have an understanding of various commonly used machine learning algorithms and will be able to deploy these algorithms on your large datasets. We aim to empower data scientists with the knowledge that is needed to effectively handle machine learning projects and provide steps to implement and evaluate these algorithms on AWS.