Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Mastering Machine Learning on AWS
  • Toc
  • feedback
Mastering Machine Learning on AWS

Mastering Machine Learning on AWS

By : Dr. Saket S.R. Mengle , Maximo Gurmendez
4.3 (8)
close
Mastering Machine Learning on AWS

Mastering Machine Learning on AWS

4.3 (8)
By: Dr. Saket S.R. Mengle , Maximo Gurmendez

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.
Table of Contents (24 chapters)
close
Free Chapter
1
Section 1: Machine Learning on AWS
3
Section 2: Implementing Machine Learning Algorithms at Scale on AWS
9
Section 3: Deep Learning
13
Section 4: Integrating Ready-Made AWS Machine Learning Services
17
Section 5: Optimizing and Deploying Models through AWS
Appendix: Getting Started with AWS

Summary

In this chapter, we looked at how to use a built-in machine learning tool called Comprehend in AWS. We briefly discussed the field of NLP and provided an introduction to its subfields, such as NER and sentiment analysis. We also examined how to create a custom document classifier in Comprehend using the dashboard it provides. Moreover, we explored how to access Comprehend's APIs using the boto3 package in Python.

These tools are fascinating as they will help you to create complex machine learning models quickly and start applying them in your applications. A data scientist who has cursory knowledge in the field of NLP can now train sophisticated machine learning models and use them to make optimal decisions. However, the question that most data scientists face is whether the pricing provided by such tools is more economical than building algorithms in-house using...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete