Data scientists and data engineers run different jobs to transform, extract, and load data into systems such as S3. For example, we might have a daily job that processes text data and stores a table with the bag-of-words table representation that we saw in Chapter 2, Classifying Twitter Feeds with Naive Bayes. We might want to update the table each day to point to the latest available data. Upstream processes can then only rely on the table name to find and process the latest version of the data. If we do not catalog this data properly, it will be very hard to combine the different data sources or even to know where the data is located, which is where the AWS Glue metastore comes in. Tables in Glue are grouped into databases; however, tables in different databases can be joined and referenced.
Mastering Machine Learning on AWS
By :
Mastering Machine Learning on AWS
By:
Overview of this book
Amazon Web Services (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 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.
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Current Title:
Mastering Machine Learning on AWS
Table of Contents (24 chapters)
Preface
Getting Started with Machine Learning for AWS
Section 2: Implementing Machine Learning Algorithms at Scale on 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
Section 3: Deep Learning
Implementing Deep Learning Algorithms
Implementing Deep Learning with TensorFlow on AWS
Image Classification and Detection with SageMaker
Section 4: Integrating Ready-Made AWS Machine Learning Services
Working with AWS Comprehend
Using AWS Rekognition
Building Conversational Interfaces Using AWS Lex
Section 5: Optimizing and Deploying Models through AWS
Creating Clusters on AWS
Optimizing Models in Spark and SageMaker
Tuning Clusters for Machine Learning
Deploying Models Built in AWS
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Appendix: Getting Started with AWS
Customer Reviews