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Learn Amazon SageMaker

You're reading from   Learn Amazon SageMaker A guide to building, training, and deploying machine learning models for developers and data scientists

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Product type Paperback
Published in Nov 2021
Publisher Packt
ISBN-13 9781801817950
Length 554 pages
Edition 2nd Edition
Languages
Tools
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Author (1):
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Julien Simon Julien Simon
Author Profile Icon Julien Simon
Julien Simon
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Introduction to Amazon SageMaker
2. Chapter 1: Introducing Amazon SageMaker FREE CHAPTER 3. Chapter 2: Handling Data Preparation Techniques 4. Section 2: Building and Training Models
5. Chapter 3: AutoML with Amazon SageMaker Autopilot 6. Chapter 4: Training Machine Learning Models 7. Chapter 5: Training CV Models 8. Chapter 6: Training Natural Language Processing Models 9. Chapter 7: Extending Machine Learning Services Using Built-In Frameworks 10. Chapter 8: Using Your Algorithms and Code 11. Section 3: Diving Deeper into Training
12. Chapter 9: Scaling Your Training Jobs 13. Chapter 10: Advanced Training Techniques 14. Section 4: Managing Models in Production
15. Chapter 11: Deploying Machine Learning Models 16. Chapter 12: Automating Machine Learning Workflows 17. Chapter 13: Optimizing Prediction Cost and Performance 18. Other Books You May Enjoy

Summary

NLP is a very exciting topic. It's also a difficult one because of the complexity of language in general, and due to how much processing is required to build datasets. Having said that, the built-in algorithms in SageMaker will help you get good results out of the box. Training and deploying models are straightforward processes, which leaves you more time to explore, understand, and prepare data.

In this chapter, you learned about the BlazingText, LDA, and NTM algorithms. You also learned how to process datasets using popular open source tools such as nltk, spaCy, and gensim, and how to save them in the appropriate format. Finally, you learned how to use the SageMaker SDK to train and deploy models with all three algorithms, as well as how to interpret results. This concludes our exploration of built-in algorithms.

In the next chapter, you will learn how to use built-in ML frameworks such as scikit-learn, TensorFlow, PyTorch, and Apache MXNet.

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