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

This book is for those who wish to understand how Python debugging is and can be used to develop robust and reliable AI, machine learning, and cloud computing software. It will teach you a novel pattern-oriented approach to diagnose and debug abnormal software structure and behavior.

The book begins with an introduction to the pattern-oriented software diagnostics and debugging process that, before performing Python debugging, diagnoses problems in various software artifacts such as memory dumps, traces, and logs. Next, you'll learn to use various debugging patterns through Python case studies that model abnormal software behavior. You'll also be exposed to Python debugging techniques specific to cloud native and machine learning environments and explore how recent advances in AI/ML can help in Python debugging. Over the course of the book, case studies will show you how to resolve issues around environmental problems, crashes, hangs, resource spikes, leaks, and performance degradation. This includes tracing, logging, and analyziing memory dumps using native WinDbg and GDB debuggers.

Upon completing this book, you will have the knowledge and tools needed to employ Python debugging in the development of AI, machine learning, and cloud computing applications.

What You Will Learn

• Employ a pattern-oriented approach to Python debugging that starts with diagnostics of common software problems
• Use tips and tricks to get the most out of popular IDEs, notebooks, and command-line Python debugging
• Understand Python internals for interfacing with operating systems and external modules
• Perform Python memory dump analysis, tracing, and logging

Who This Book Is For

Software developers, AI/ML engineers, researchers, data engineers, as well as MLOps and DevOps professionals.

這本書適合那些希望了解如何使用Python調試來開發強大可靠的人工智慧、機器學習和雲端運算軟體的讀者。它將教授您一種新穎的模式導向方法，用於診斷和調試異常的軟體結構和行為。

本書首先介紹了模式導向軟體診斷和調試流程，該流程在進行Python調試之前，會診斷各種軟體工件（如內存轉儲、追蹤和日誌）中的問題。接下來，您將通過Python案例研究學習使用各種調試模式，這些模式模擬異常的軟體行為。您還將接觸到特定於雲原生和機器學習環境的Python調試技術，並探索人工智慧/機器學習的最新進展如何幫助Python調試。在本書的過程中，案例研究將向您展示如何解決環境問題、崩潰、卡住、資源峰值、洩漏和性能下降等問題。這包括使用本地WinDbg和GDB調試器進行追蹤、日誌記錄和分析內存轉儲。

完成本書後，您將具備在開發人工智慧、機器學習和雲端運算應用程序中使用Python調試所需的知識和工具。

您將學到什麼：
- 使用模式導向方法進行Python調試，從診斷常見軟體問題開始
- 使用技巧和訣竅充分利用流行的IDE、筆記本和命令行Python調試工具
- 了解Python內部與操作系統和外部模組的接口
- 執行Python內存轉儲分析、追蹤和日誌記錄

本書適合軟體開發人員、人工智慧/機器學習工程師、研究人員、數據工程師，以及MLOps和DevOps專業人員。

Dmitry Vostokov is an internationally recognized expert, speaker, educator, scientist, inventor, and author. He founded the pattern-oriented software diagnostics, forensics, and prognostics discipline (Systematic Software Diagnostics) and Software Diagnostics Institute (DA+TA: DumpAnalysis.org + TraceAnalysis.org). Vostokov has also authored multiple books on software diagnostics, anomaly detection and analysis, software, and memory forensics, root cause analysis and problem-solving, memory dump analysis, debugging, software trace and log analysis, reverse engineering, and malware analysis. He has over thirty years of experience in software architecture, design, development, and maintenance in various industries, including leadership, technical, and people management roles. In his spare time, he presents multiple topics on Debugging.TV and explores Software Narratology and its further development as Narratology of Things and Diagnostics of Things (DoT), Software Pathology, and Quantum Software Diagnostics. His current interest areas are theoretical software diagnostics and its mathematical and computer science foundations, application of formal logic, artificial intelligence, machine learning, and data mining to diagnostics and anomaly detection, software diagnostics engineering and diagnostics-driven development, diagnostics workflow, and interaction. Recent interest areas also include cloud native computing, security, automation, functional programming, applications of category theory to software development and big data, and artificial intelligence diagnostics.

Dmitry Vostokov 是一位國際知名的專家、演講者、教育家、科學家、發明家和作家。他創立了以模式為導向的軟體診斷、取證和預測學科（系統化軟體診斷）以及軟體診斷研究所（DA+TA: DumpAnalysis.org + TraceAnalysis.org）。Vostokov還撰寫了多本關於軟體診斷、異常檢測和分析、軟體和記憶體取證、根本原因分析和問題解決、記憶體轉儲分析、除錯、軟體追蹤和日誌分析、逆向工程和惡意軟體分析的書籍。他在軟體架構、設計、開發和維護等多個行業擁有超過三十年的經驗，包括領導、技術和人員管理角色。在閒暇時間，他在Debugging.TV上演講多個主題，並探索軟體敘事學及其作為事物敘事學和診斷學（DoT）、軟體病理學和量子軟體診斷的進一步發展。他目前的研究興趣包括理論軟體診斷及其數學和計算機科學基礎、形式邏輯、人工智慧、機器學習和數據挖掘在診斷和異常檢測中的應用、軟體診斷工程和診斷驅動開發、診斷工作流程和互動。最近的研究興趣還包括原生雲計算、安全性、自動化、函數式編程、范畴論在軟體開發和大數據中的應用，以及人工智慧診斷。