Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Machine Learning Engineering on AWS

You're reading from   Machine Learning Engineering on AWS Build, scale, and secure machine learning systems and MLOps pipelines in production

Arrow left icon
Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803247595
Length 530 pages
Edition 1st Edition
Tools
Arrow right icon
Author (1):
Arrow left icon
Joshua Arvin Lat Joshua Arvin Lat
Author Profile Icon Joshua Arvin Lat
Joshua Arvin Lat
Arrow right icon
View More author details
Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1: Getting Started with Machine Learning Engineering on AWS
2. Chapter 1: Introduction to ML Engineering on AWS FREE CHAPTER 3. Chapter 2: Deep Learning AMIs 4. Chapter 3: Deep Learning Containers 5. Part 2:Solving Data Engineering and Analysis Requirements
6. Chapter 4: Serverless Data Management on AWS 7. Chapter 5: Pragmatic Data Processing and Analysis 8. Part 3: Diving Deeper with Relevant Model Training and Deployment Solutions
9. Chapter 6: SageMaker Training and Debugging Solutions 10. Chapter 7: SageMaker Deployment Solutions 11. Part 4:Securing, Monitoring, and Managing Machine Learning Systems and Environments
12. Chapter 8: Model Monitoring and Management Solutions 13. Chapter 9: Security, Governance, and Compliance Strategies 14. Part 5:Designing and Building End-to-end MLOps Pipelines
15. Chapter 10: Machine Learning Pipelines with Kubeflow on Amazon EKS 16. Chapter 11: Machine Learning Pipelines with SageMaker Pipelines 17. Index 18. Other Books You May Enjoy

Running our first Kubeflow pipeline

In this section, we will run a custom pipeline that will download a sample tabular dataset and use it as training data to build our linear regression model. The steps and instructions to be executed by the pipeline have been defined inside a YAML file. Once this YAML file has been uploaded, we would then be able to run a Kubeflow pipeline that will run the following steps:

  1. Download dataset: Here, we will be downloading and working with a dataset that only has 20 records (along with the row containing the header). In addition to this, we will start with a clean version without any missing or invalid values:

Figure 10.16 – A sample tabular dataset

In Figure 10.16, we can see that our dataset has three columns:

  • last_name – This is the last name of the manager.
  • management_experience_months – This is the total number of months a manager has been managing team members.
  • monthly_salary...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime