Mastering Machine Learning on AWS

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  • 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

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.

  • Build machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark, and TensorFlow
  • Learn model optimization and understand how to scale your models using simple and secure APIs
  • Develop, train, tune, and deploy neural network models to accelerate model performance in the cloud
Page Count 306
Course Length 9 hours 10 minutes
ISBN 9781789349795
Date Of Publication 20 May 2019
Predicting the price of houses
Understanding linear regression
Evaluating regression models 
Implementing linear regression through scikit-learn
Implementing linear regression through Apache Spark
Implementing linear regression through SageMaker's linear Learner
Understanding logistic regression
Pros and cons of linear models
Understanding decision trees
Understanding random forest algorithms
Understanding gradient boosting algorithms
Predicting clicks on log streams