Mastering Machine Learning on AWS : Advanced Machine Learning in Python Using SageMaker, Apache Spark, and TensorFlow

Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key Features 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 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 Getting started with Machine learning for AWS Classifying Twitter Feeds with Naive Bayes Predicting House Value with Regression Algorithms Predicting User Behavior with Tree-based Methods Customer Segmentation Using Clustering Algorithms Analyzing Visitor Patterns to Make Recommendations Implementing Deep Learning Algorithms Implementing Deep Learning with TensorFlow on AWS Image Classification and Detection with Sagemaker Working with AWS Comprehend Using AWS Rekognition Building Conversational Interfaces Using AWS Lex Creating Clusters on AWS Optimizing Models in Spark and Sagemaker Tuning clusters for Machine Learning Deploying models built on AWS Cover Title Page Copyright and Credits Dedication About Packt Contributors Table of Contents Preface Section 1: Machine Learning on AWS Chapter 1: Getting Started with Machine Learning for AWS How AWS empowers data scientists Using AWS tools for machine learning Identifying candidate problems that can be solved using machine learning Machine learning project life cycle Data gathering Evaluation metrics Algorithm selection Deploying models Summary Exercise Section 2: Implementing Machine Learning Algorithms at Scale on AWS Chapter 2: Classifying Twitter Feeds with Naive Bayes Classification algorithms Feature types Nominal features Ordinal features Continuous features Naive Bayes classifier Bayes' theorem Posterior Likelihood Prior probability Evidence How the Naive Bayes algorithm works Classifying text with language models Collecting the tweets Preparing the data Building a Naive Bayes model through SageMaker notebooks Naïve Bayes model on SageMaker notebooks using Apache Spark Using SageMaker's BlazingText built-in ML service Naive Bayes – pros and cons Summary Exercises Chapter 3: Predicting House Value with Regression Algorithms Predicting the price of houses Understanding linear regression Linear least squares estimation Maximum likelihood estimation Gradient descent Evaluating regression models Mean absolute error Mean squared error Root mean squared error R-squared Implementing linear regression through scikit-learn Implementing linear regression through Apache Spark Implementing linear regression through SageMaker's linear Learner Understanding logistic regression Logistic regression in Spark Pros and cons of linear models Summary Chapter 4: Predicting User Behavior with Tree-Based Methods Understanding decision trees Recursive splitting Types of decision trees Cost functions Gini Impurity Information gain Criteria to stop splitting trees Understanding random forest algorithms Understanding gradient boosting algorithms Predicting clicks on log streams Introduction to Elastic MapReduce (EMR) Training with Apache Spark on EMR Getting the data Preparing the data Categorical encoding One-hot encoding Training a model Evaluating our model Area Under ROC Curve Area under the precision-recall curve Training tree ensembles on EMR Training gradient-boosted trees with the SageMaker services Preparing the data Training with SageMaker XGBoost Applying and evaluating the model Summary Exercises Chapter 5: Customer Segmentation Using Clustering Algorithms Understanding How Clustering Algorithms Work k-means clustering Euclidean distance Manhattan distance Hierarchical clustering Agglomerative clustering Divisive clustering Clustering with Apache Spark on EMR Clustering with Spark and SageMaker on EMR Understanding the purpose of the IAM role Summary Exercises Chapter 6: Analyzing Visitor Patterns to Make Recommendations Making theme park attraction recommendations through Flickr data Collaborative filtering Memory-based approach Model-based approach Matrix factorization Stochastic gradient descent Alternating Least Squares Finding recommendations through Apache Spark's ALS Data gathering and exploration Training the model Getting recommendations Recommending attractions through SageMaker Factorization Machines Preparing the dataset for learning Training the model Getting recommendations Summary Exercises Section 3: Deep Learning Chapter 7: Implementing Deep Learning Algorithms Understanding deep learning Applications of deep learning Self-driving cars Learning to play video games using a deep learning algorithm Understanding deep learning algorithms Neural network algorithms Activation function Backpropagation Introduction to deep neural networks Understanding convolutional neural networks Summary Exercises Chapter 8: Implementing Deep Learning with TensorFlow on AWS About TensorFlow TensorFlow as a general machine learning library Training and serving the TensorFlow model through SageMaker Creating a custom neural net with TensorFlow Summary Exercises Chapter 9: Image Classification and Detection with SageMaker Introducing Amazon SageMaker for image classification Training a deep learning model using Amazon SageMaker Classifying images using Amazon SageMaker Summary Exercises Section 4: Integrating Ready-Made AWS Machine Learning Services Chapter 10: Working with AWS Comprehend Introducing Amazon Comprehend Accessing AmazonComprehend Named-entity recognition using Comprehend Sentiment analysis using Comprehend Text classification using Comprehend Summary Exercise Chapter 11: Using AWS Rekognition Introducing Amazon Rekognition Implementing object and scene detection Implementing facial analysis Other Rekognition services Image moderation Celebrity recognition Face comparison Summary Exercise Chapter 12: Building Conversational Interfaces Using AWS Lex Introducing Amazon Lex Building a custom chatbot using Amazon Lex Summary Exercises Section 5: Optimizing and Deploying Models through AWS Chapter 13: Creating Clusters on AWS Choosing your instance types On-demand versus spot instance pricing Reserved pricing Amazon Machine Images (AMIs) Deep learning hardware Distributed deep learning Model versus data parallelization Distributed TensorFlow Distributed learning through Apache Spark Data parallelization Model parallelization Distributed hyperparameter tuning Distributed predictions at scale Parallelization in SageMaker Summary Chapter 14: Optimizing Models in Spark and SageMaker The importance of model optimization Automatic hyperparameter tuning Hyperparameter tuning in Apache Spark Hyperparameter tuning in SageMaker Summary Exercises Chapter 15: Tuning Clusters for Machine Learning Introduction to the EMR architecture Apache Hadoop Apache Spark Apache Hive Presto Apache HBase Yet Another Resource Negotiator Tuning EMR for different applications Configuring application properties Maximize Resource Allocation The AWS Glue Catalog Managing data pipelines with Glue Creating tables with Glue Accessing Glue tables in Spark Summary Chapter 16: Deploying Models Built in AWS SageMaker model deployment Apache Spark model deployment Summary Exercises Appendix: Getting Started with AWS Other Books You May Enjoy Index **Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow.** ## Key Features * 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 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 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. 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 This book will help you master your skills in various artificial intelligence and machine learning services available on AWS. Through practical hands-on examples, you'll learn how to use these services to generate impressive results. You will have a tremendous understanding of how to use a wide range of AWS services in your own organization.