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Production-Ready Applied Deep Learning

You're reading from Production-Ready Applied Deep Learning Learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks

Product type Paperback

Published in Aug 2022

Publisher Packt

ISBN-13 9781803243665

Length 322 pages

Edition 1st Edition

Tools

PyTorch

Concepts

Deep Learning

Authors (3):

Lenin Mookiah

Tomasz Palczewski

Jaejun (Brandon) Lee

View More author details

Table of Contents (19) Chapters

Preface

1. Part 1 – Building a Minimum Viable Product

2. Chapter 1: Effective Planning of Deep Learning-Driven Projects FREE CHAPTER

3. Chapter 2: Data Preparation for Deep Learning Projects

4. Chapter 3: Developing a Powerful Deep Learning Model

5. Chapter 4: Experiment Tracking, Model Management, and Dataset Versioning

6. Part 2 – Building a Fully Featured Product

7. Chapter 5: Data Preparation in the Cloud

8. Chapter 6: Efficient Model Training

9. Chapter 7: Revealing the Secret of Deep Learning Models

10. Part 3 – Deployment and Maintenance

11. Chapter 8: Simplifying Deep Learning Model Deployment

12. Chapter 9: Scaling a Deep Learning Pipeline

13. Chapter 10: Improving Inference Efficiency

14. Chapter 11: Deep Learning on Mobile Devices

15. Chapter 12: Monitoring Deep Learning Endpoints in Production

16. Chapter 13: Reviewing the Completed Deep Learning Project

17. Index

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18. Other Books You May Enjoy

Summary

One of the difficulties with DL projects arises from the amount of data. Since a large amount of data is necessary to train a DL model, data processing steps can take up a lot of resources. Therefore, in this chapter, we learned how to utilize the most popular cloud service, AWS, to process terabytes and petabytes of data efficiently. The system includes a scheduler, data storage, databases, visualization, as well as a data processing tool for running the ETL logic.

We have spent extra time looking at ETL since it plays a major role in data processing. We introduced Spark, which is the most popular tool for ETL, and described four different ways of setting up ETL jobs using AWS. The four settings include using a single-node EC2 instance, an EMR cluster, Glue, and SageMaker. Each setup has distinct advantages, and the right one may differ based on the situation. This is because you need to consider both technical and non-technical aspects of the project.

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Authors (3)

Tomasz Palczewski

Tomasz Palczewski

Tomasz Palczewski is currently working as a staff software engineer at Samsung Research America. He has a Ph.D. in physics and an eMBA degree from Quantic. His zeal for getting insights out of large datasets using cutting-edge techniques led him to work across the globe at CERN (Switzerland), LBNL (Italy), J-PARC (Japan), University of Alabama (US), and University of California (US). In 2016, he was deployed to the South Pole to calibrate the world's largest neutrino telescope. Later, he decided to pivot his career and focus on applying his skills in industry. Currently, he works on modeling user behavior and creating value for advertising and marketing verticals by deploying machine learning (ML), deep learning, and statistical models at scale.

See other products by Tomasz Palczewski

Lenin Mookiah

Lenin Mookiah

Lenin Mookiah is a machine learning engineer who has worked with reputed tech companies – Samsung Research America, eBay Inc., and Adobe R&D. He has worked in the technology industry for over 11 years in various domains: banking, retail, eDiscovery, and media. He has played various roles in the end-to-end productization of large-scale machine learning systems. He mainly employs the big data ecosystem to build reliable feature pipelines that data scientists consume. Apart from his industrial experience, he researched anomaly detection in his Ph.D. at Tennessee Tech University (US) using a novel graph-based approach. He studied entity resolution on social networks during his master's at Tsinghua University, China.

See other products by Lenin Mookiah

Jaejun (Brandon) Lee

Jaejun (Brandon) Lee

Jaejun (Brandon) Lee is currently working as an AI research lead at RoboEye.ai, integrating cutting-edge algorithms in computer vision and AI into industrial automation solutions. He has obtained his master's degree from the University of Waterloo with research focused on natural language processing (NLP), specifically speech recognition. He has spent many years developing a fully productionized yet open source wake word detection toolkit with a web browser deployment target, Howl. Luckily, his effort has been picked up by Mozilla's Firefox Voice and it is actively providing a completely hands-free experience to many users all over the world.

See other products by Jaejun (Brandon) Lee

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