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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

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781803243665
Length 322 pages
Edition 1st Edition
Tools
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Authors (3):
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Lenin Mookiah Lenin Mookiah
Author Profile Icon Lenin Mookiah
Lenin Mookiah
Tomasz Palczewski Tomasz Palczewski
Author Profile Icon Tomasz Palczewski
Tomasz Palczewski
Jaejun (Brandon) Lee Jaejun (Brandon) Lee
Author Profile Icon Jaejun (Brandon) Lee
Jaejun (Brandon) Lee
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Toc

Table of Contents (19) Chapters Close

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 18. Other Books You May Enjoy

Summary

This chapter is an introduction to our journey. In the first two sections, we have described where DL sits within the wider picture of AI and how it continually shapes our daily lives. The key takeaways are the fact that DL is highly flexible due to its unique model architecture and the fact that DL has been actively adopted to the domain which traditional ML techniques have failed to demonstrate notable accomplishments.

Then, we have provided a high-level view of the DL project. In general, DL projects can be split into the following phases: project planning, building MVPs, building FFPs, development and maintenance, and project evaluation.

The main contents of this chapter covered the most important step of the DL project: project planning. In this phase, the purpose of the project needs to be clearly defined, along with the evaluation metrics, everyone must have a solid understanding of the stakeholders and their respective roles, and lastly, the tasks, milestones, and timeline need to be agreed upon by the participants. The outcome of this phase would be a well-formatted document called a playbook. In the next chapter, we will learn how to prepare data for DL projects.

You have been reading a chapter from
Production-Ready Applied Deep Learning
Published in: Aug 2022
Publisher: Packt
ISBN-13: 9781803243665
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