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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

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803247595
Length 530 pages
Edition 1st Edition
Tools
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Author (1):
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Joshua Arvin Lat Joshua Arvin Lat
Author Profile Icon Joshua Arvin Lat
Joshua Arvin Lat
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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

Loading and evaluating the model

In the previous section, we trained our deep learning model using the terminal. When performing ML experiments, it is generally more convenient to use a web-based interactive environment such as the Jupyter Notebook. We can technically run all the succeeding code blocks in the terminal, but we will use the Jupyter Notebook instead for convenience.

In the next set of steps, we will launch the Jupyter Notebook from the command line. Then, we will run a couple of blocks of code to load and evaluate the ML model we trained in the previous section. Let’s get started:

  1. Continuing where we left off in the Training an ML model section, let’s run the following command in the EC2 Instance Connect terminal:
    jupyter notebook --allow-root --port 8888 --ip 0.0.0.0

This should start the Jupyter Notebook and make it accessible through port 8888:

Figure 2.31 – Jupyter Notebook token

Make sure that you copy...

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