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Generative AI-Powered Assistant for Developers

You're reading from   Generative AI-Powered Assistant for Developers Accelerate software development with Amazon Q Developer

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835089149
Length 416 pages
Edition 1st Edition
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Authors (2):
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Behram Irani Behram Irani
Author Profile Icon Behram Irani
Behram Irani
LinkedIn
Behram Irani is currently a technology leader at Amazon Web Services (AWS), specializing in data, analytics, and AI/ML. With over 20 years in the tech industry, he has helped organizations ranging from start-ups to large-scale enterprises modernize their data platforms. In the last 7 years at AWS, Behram has been a thought leader in the data, analytics, and AI/ML space. He has published a book and multiple papers and has led digital transformation efforts for many companies worldwide. Behram holds a Bachelor of Engineering in Computer Science from the University of Pune and an MBA from the University of Florida. He is also the author of Modern Data Architecture on AWS.
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See other products by Behram Irani
Rahul Sonawane Rahul Sonawane
Author Profile Icon Rahul Sonawane
Rahul Sonawane
LinkedIn
Rahul Sonawane is currently a specialist solution architect at AWS specializing in data, analytics, and AI/ML. With over 24 years of technology experience, he has worked across multiple industries globally, including retail, consulting, and large-scale enterprises. Before joining AWS, Rahul worked on Google Cloud Platform for 2 years. For the last 7 years at AWS, he has been helping organizations modernize their data platforms. Rahul has published multiple public blog posts and articles focusing on data, analytics, and AI/ML. He holds a Master of Engineering in Computer Science from the University of Pune with a specialization in data warehouse technologies.
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Toc

Table of Contents (23) Chapters Close

Preface 1. Part 1:Generative AI-Powered Assistant FREE CHAPTER
2. Chapter 1: Introduction to Generative AI-Powered Assistants 3. Chapter 2: Introducing and Setting Up Amazon Q Developer 4. Part 2:Generate Code Recommendations
5. Chapter 3: Understanding Auto-Code Generation Techniques 6. Chapter 4: Boost Coding Efficiency for Python and Java with Auto-Code Generation 7. Chapter 5: Boost Coding Efficiency for C and C++ with Auto-Code Generation 8. Chapter 6: Boost Coding Efficiency for JavaScript and PHP with Auto-Code Generation 9. Chapter 7: Boost Coding Efficiency for SQL with Auto-Code Generation 10. Chapter 8: Boost Coding Efficiency for Command-Line and Shell Script with Auto-Code Generation 11. Chapter 9: Boost Coding Efficiency for JSON, YAML, and HCL with Auto-Code Generation 12. Part 3:Advanced Assistant Features
13. Chapter 10: Customizing Code Recommendations 14. Chapter 11: Understanding Code References 15. Chapter 12: Simplifying Code Explanation, Optimization, Transformation, and Feature Development 16. Chapter 13: Simplifying Scanning and Fixing Security Vulnerabilities in Code 17. Part 4:Accelerate Build on AWS
18. Chapter 14: Accelerate Data Engineering on AWS 19. Chapter 15: Accelerate Building Solutions on AWS 20. Chapter 16: Accelerate the DevOps Process on AWS 21. Index 22. Other Books You May Enjoy

Part 2:Generate Code Recommendations

In this part, we will look at many of the key features of Amazon Q Developer that can assist developers during the software development life cycle. These features can be used within many of the supported IDEs and assist with various programming languages.

This part contains the following chapters:

  • Chapter 3, Understanding Auto-Code Generation Techniques
  • Chapter 4, Boost Coding Efficiency for Python and Java with Auto-Code Generation
  • Chapter 5, Boost Coding Efficiency for C and C++ with Auto-Code Generation
  • Chapter 6, Boost Coding Efficiency for JavaScript and PHP with Auto-Code Generation
  • Chapter 7, Boost Coding Efficiency for SQL with Auto-Code Generation
  • Chapter 8, Boost Coding Efficiency for Command-Line and Shell Script with Auto-Code Generation
  • Chapter 9, Boost Coding Efficiency for JSON, YAML, and HCL with Auto-Code Generation
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Authors (2)

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Profile icon Behram Irani
Behram Irani
LinkedIn icon
Behram Irani is currently a technology leader at Amazon Web Services (AWS), specializing in data, analytics, and AI/ML. With over 20 years in the tech industry, he has helped organizations ranging from start-ups to large-scale enterprises modernize their data platforms. In the last 7 years at AWS, Behram has been a thought leader in the data, analytics, and AI/ML space. He has published a book and multiple papers and has led digital transformation efforts for many companies worldwide. Behram holds a Bachelor of Engineering in Computer Science from the University of Pune and an MBA from the University of Florida. He is also the author of Modern Data Architecture on AWS.
Read more
See other products by Behram Irani
Profile icon Rahul Sonawane
Rahul Sonawane
LinkedIn icon
Rahul Sonawane is currently a specialist solution architect at AWS specializing in data, analytics, and AI/ML. With over 24 years of technology experience, he has worked across multiple industries globally, including retail, consulting, and large-scale enterprises. Before joining AWS, Rahul worked on Google Cloud Platform for 2 years. For the last 7 years at AWS, he has been helping organizations modernize their data platforms. Rahul has published multiple public blog posts and articles focusing on data, analytics, and AI/ML. He holds a Master of Engineering in Computer Science from the University of Pune with a specialization in data warehouse technologies.
Read more
See other products by Rahul Sonawane
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