Python Debugging for AI, Machine Learning, and Cloud Computing: A Pattern-Oriented Approach

This book teaches a pattern-oriented approach to Python debugging for developing robust and reliable AI, machine learning, and cloud computing software. It covers diagnosing problems in software artifacts, using debugging patterns, and techniques specific to cloud native and machine learning environments. Case studies demonstrate how to resolve issues such as environmental problems, crashes, hangs, resource spikes, leaks, and performance degradation.

Format: Paperback / softback
Length: 233 pages
Publication date: 30 November 2023
Publisher: APress

This comprehensive book is designed to empower individuals with the knowledge and skills necessary to excel in Python debugging, enabling them to develop robust and reliable AI, machine learning, and cloud computing software. It offers a novel pattern-oriented approach to diagnosing and debugging abnormal software structure and behavior, providing a comprehensive guide for software developers, AI/ML engineers, researchers, data engineers, MLOps, and DevOps professionals.

The book begins by introducing the pattern-oriented software diagnostics and debugging process, which serves as a foundation for Python debugging. It then delves into the diagnosis of problems in various software artifacts, including memory dumps, traces, and logs, before moving on to explore the practical application of debugging patterns through Python case studies. These case studies showcase abnormal software behavior and demonstrate how to resolve issues related to environmental problems, crashes, hangs, resource spikes, leaks, and performance degradation.

Throughout the book, readers are exposed to a range of Python debugging techniques tailored for cloud native and machine learning environments. It highlights how recent advances in AI/ML can be leveraged to enhance Python debugging capabilities. By the end of the book, readers will have gained a deep understanding of Python debugging principles and will be equipped with the knowledge and tools needed to employ Python debugging in the development of AI, machine learning, and cloud computing applications.

Key Features:

A Pattern-Oriented Approach: The book adopts a pattern-oriented approach to Python debugging, which helps readers understand and apply best practices in a systematic manner. It provides a comprehensive set of patterns that can be used to diagnose and debug a wide range of software problems.

Diagnostics of Common Software Problems: The book begins by covering the diagnosis of common software problems, such as memory leaks, resource contention, and performance issues. It provides practical tips and tricks to identify and resolve these problems effectively.

IDEs, Notebooks, and Command-Line Debugging: Readers are introduced to popular IDEs, notebooks, and command-line Python debugging tools, including PyCharm, Jupyter Notebook, and the Python debugger. It teaches users how to optimize their debugging workflow and leverage the features of these tools to enhance their debugging capabilities.

Python Internals for Interfacing with Operating Systems and External Modules: The book delves into Python internals, providing insights into how Python interacts with operating systems and external modules. It explains how to perform memory dump analysis, trace Python code execution, and log debugging information.

Memory Dump Analysis, Tracing, and Logging: Readers are guided through the process of analyzing memory dumps, tracing Python code execution, and logging debugging information. It covers the use of native WinDbg and GDB debuggers to perform comprehensive analysis of software problems.

Who This Book Is For:

This book is designed for software developers, AI/ML engineers, researchers, data engineers, MLOps, and DevOps professionals who are interested in enhancing their Python debugging skills. It provides a comprehensive coverage of Python debugging principles and techniques, making it an invaluable resource for anyone working on AI, machine learning, and cloud computing projects.

In conclusion, this book is a must-read for anyone seeking to improve their Python debugging skills and develop robust and reliable AI, machine learning, and cloud computing software. Its pattern-oriented approach, comprehensive coverage of debugging techniques, and practical insights make it an invaluable resource for software developers, AI/ML engineers, researchers, data engineers, MLOps, and DevOps professionals. By mastering Python debugging, individuals can unlock the full potential of these technologies and contribute to the advancement of the field.

Weight: 493g
Dimension: 254 x 178 (mm)
ISBN-13: 9781484297445
Edition number: 1st ed.