Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
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
The Machine Learning Solutions Architect Handbook

You're reading from   The Machine Learning Solutions Architect Handbook Create machine learning platforms to run solutions in an enterprise setting

Arrow left icon
Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781801072168
Length 442 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
David Ping David Ping
Author Profile Icon David Ping
David Ping
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Solving Business Challenges with Machine Learning Solution Architecture
2. Chapter 1: Machine Learning and Machine Learning Solutions Architecture FREE CHAPTER 3. Chapter 2: Business Use Cases for Machine Learning 4. Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
5. Chapter 3: Machine Learning Algorithms 6. Chapter 4: Data Management for Machine Learning 7. Chapter 5: Open Source Machine Learning Libraries 8. Chapter 6: Kubernetes Container Orchestration Infrastructure Management 9. Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms
10. Chapter 7: Open Source Machine Learning Platforms 11. Chapter 8: Building a Data Science Environment Using AWS ML Services 12. Chapter 9: Building an Enterprise ML Architecture with AWS ML Services 13. Chapter 10: Advanced ML Engineering 14. Chapter 11: ML Governance, Bias, Explainability, and Privacy 15. Chapter 12: Building ML Solutions with AWS AI Services 16. Other Books You May Enjoy

Hands-on exercise – building an MLOps pipeline on AWS

In this hands-on exercise, you will get hands on with building a simplified version of the enterprise MLOps pipeline. For simplicity, we will not be using the multi-account architecture for the enterprise pattern. Instead, we will build several core functions in a single AWS account. The following diagram shows what you will be building:

Figure 9.10 – Architecture of the hands-on exercise

At a high level, you will create two pipelines using CloudFormation: one for model training and one for model deployment.

Creating a CloudFormation template for the ML training pipeline

In this section, we will create two CloudFormation templates that do the following:

  • The first template creates AWS Step Functions for an ML model training workflow that performs data processing, model training, and model registration. This will be a component of the training pipeline.
  • The second template...
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 $19.99/month. Cancel anytime