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Data Wrangling on AWS

You're reading from   Data Wrangling on AWS Clean and organize complex data for analysis

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Product type Paperback
Published in Jul 2023
Publisher Packt
ISBN-13 9781801810906
Length 420 pages
Edition 1st Edition
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Authors (3):
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Sankar M Sankar M
Author Profile Icon Sankar M
Sankar M
Navnit Shukla Navnit Shukla
Author Profile Icon Navnit Shukla
Navnit Shukla
Sam Palani Sam Palani
Author Profile Icon Sam Palani
Sam Palani
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1:Unleashing Data Wrangling with AWS
2. Chapter 1: Getting Started with Data Wrangling FREE CHAPTER 3. Part 2:Data Wrangling with AWS Tools
4. Chapter 2: Introduction to AWS Glue DataBrew 5. Chapter 3: Introducing AWS SDK for pandas 6. Chapter 4: Introduction to SageMaker Data Wrangler 7. Part 3:AWS Data Management and Analysis
8. Chapter 5: Working with Amazon S3 9. Chapter 6: Working with AWS Glue 10. Chapter 7: Working with Athena 11. Chapter 8: Working with QuickSight 12. Part 4:Advanced Data Manipulation and ML Data Optimization
13. Chapter 9: Building an End-to-End Data-Wrangling Pipeline with AWS SDK for Pandas 14. Chapter 10: Data Processing for Machine Learning with SageMaker Data Wrangler 15. Part 5:Ensuring Data Lake Security and Monitoring
16. Chapter 11: Data Lake Security and Monitoring 17. Index 18. Other Books You May Enjoy

Training a machine learning model

We have now used Data Wrangler to do data analysis and processing, which involved several steps, such as data cleaning, preprocessing, feature engineering, and exploratory data analysis. These steps are crucial before doing machine learning, as they ensure that data is in the correct format, we selected the relevant features, and we dealt with outliers and missing values using data transformation. Data Wrangler provides a unified experience, enabling you to prepare data and seamlessly train a machine learning model, all from within the tool.

SageMaker Autopilot is a tool that automates the key tasks of an automatic machine learning (AutoML) process. This includes exploring your data, selecting algorithms relevant to your problem type, and preparing the data to facilitate model training and tuning. With just a few clicks, you can automatically build, train, and tune ML models using Autopilot, XGBoost, or your own algorithm, directly from the Data...

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