Mastering Machine Learning on AWS: Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow

Book Description

AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud.

As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis.

By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS.

What you will learn

• Manage AI workflows by 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 EMR
• Understand how to cluster big data through EMR and seamlessly integrate it with SageMaker
• Build deep learning models on AWS using TensorFlow and deploy them as services
• Enhance your apps by combining Apache Spark and Amazon SageMaker

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 using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.

Table of Contents

1. Getting started with Machine learning for AWS
2. Classifying Twitter Feeds with Naive Bayes
3. Predicting House Value with Regression Algorithms
4. Predicting User Behavior with Tree-based Methods
5. Customer Segmentation Using Clustering Algorithms
6. Analyzing Visitor Patterns to Make Recommendations
7. Implementing Deep Learning Algorithms
8. Implementing Deep Learning with TensorFlow on AWS
9. Image Classification and Detection with Sagemaker
10. Working with AWS Comprehend
11. Using AWS Rekognition
12. Building Conversational Interfaces Using AWS Lex
13. Creating Clusters on AWS
14. Optimizing Models in Spark and Sagemaker
15. Tuning clusters for Machine Learning
16. Deploying models built on AWS