You're reading from Getting Started with Amazon SageMaker Studio Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

Product type Paperback

Published in Mar 2022

Publisher Packt

ISBN-13 9781801070157

Length 326 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Michael Hsieh

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Table of Contents (16) Chapters

Preface

1. Part 1 – Introduction to Machine Learning on Amazon SageMaker Studio

2. Chapter 1: Machine Learning and Its Life Cycle in the Cloud FREE CHAPTER

3. Chapter 2: Introducing Amazon SageMaker Studio

4. Part 2 – End-to-End Machine Learning Life Cycle with SageMaker Studio

5. Chapter 3: Data Preparation with SageMaker Data Wrangler

6. Chapter 4: Building a Feature Repository with SageMaker Feature Store

7. Chapter 5: Building and Training ML Models with SageMaker Studio IDE

8. Chapter 6: Detecting ML Bias and Explaining Models with SageMaker Clarify

9. Chapter 7: Hosting ML Models in the Cloud: Best Practices

10. Chapter 8: Jumpstarting ML with SageMaker JumpStart and Autopilot

11. Part 3 – The Production and Operation of Machine Learning with SageMaker Studio

12. Chapter 9: Training ML Models at Scale in SageMaker Studio

13. Chapter 10: Monitoring ML Models in Production with SageMaker Model Monitor

14. Chapter 11: Operationalize ML Projects with SageMaker Projects, Pipelines, and Model Registry

15. Other Books You May Enjoy

Getting started with SageMaker Feature Store

Following the scenario we described earlier, we are a data science team in a company in the automotive industry. We are working on a fuel efficiency dataset to create ML models. Let's use an Auto MPG dataset from UCI (https://archive.ics.uci.edu/ml/datasets/Auto+MPG), which is a collection of vehicle data and fuel efficiency (measured by miles per gallon) by make from 1970 to 1982, to demonstrate the following:

How to ingest features into a feature store
How to access features from an online store and an offline store
How to update the features year over year and access features using versioning (time travel)

As a prerequisite, please navigate to the code repository and open the chapter04/01-sagemaker_feature_store.ipynb notebook. First, execute the notebook until the following code to read the data from the source into a pandas DataFrame:

data_url='https://archive.ics.uci.edu/ml/machine-learning-databases...

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You're reading from Getting Started with Amazon SageMaker Studio Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

Table of Contents (16) Chapters

Getting started with SageMaker Feature Store

Authors (1)

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You're reading from Getting Started with Amazon SageMaker Studio Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

Table of Contents (16) Chapters

Getting started with SageMaker Feature Store

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