- Prologue: What We're Building
- Part I — Foundations
- Chapter 1: The Data Science Landscape
- Chapter 2: The Working Engineer's Python
- Chapter 3: The Statistics You Actually Need
- Chapter 4: Data — Types, Sources, Ethics
- Part II — Data to Insight
- Chapter 5: Data Collection — Building the TalentLens Dataset
- Chapter 6: Data Cleaning — Turning Raw Into Usable
- Chapter 7: Exploratory Data Analysis
- Chapter 8: Statistical Inference — Testing What You Noticed
- Part III — Classical ML
- Chapter 9: Supervised Learning — The TalentLens Role Classifier
- Chapter 10: Feature Engineering and Selection
- Chapter 11: Unsupervised Learning — Clustering the TalentLens Corpus
- Chapter 12: Neural Networks — When Depth Pays for Itself
- Part IV — Specialist AI
- Chapter 13: NLP — Extracting Skills from Job Postings
- Chapter 14: Time Series — Skill Trends (Methods First)
- Chapter 15: Scaling Python — When You Outgrow Pandas
- Part V — The GenAI Stack
- Chapter 16: RAG and Vector Search — Building Intelligent Search
- Chapter 17: LLM Generation — Making TalentLens Explain Itself
- Chapter 18: Agentic AI — Letting the Model Choose Its Tools
- Part VI — Shipping
- Chapter 19: FastAPI — Ship TalentLens as an API
- Chapter 20: Docker + Render — Ship the API in a Container
- Chapter 21: CI/CD — Automating Tests, Quality, and Deployment
- Chapter 22: Packaging as a PyPI Library
- Part VII — Career
- Chapter 23: Real-World Case Studies
- Chapter 24: The India Playbook — Getting Hired, Paid Well, and Building a Global Career from India
- Glossary
- References and further reading
Data Voyage
Building Real AI Systems from Data to Deployment
This book is 100% completeLast updated on 2026-10-02
Build one real AI system end to end, from raw job postings to a deployed, tested API, and learn production AI engineering on the way. 24 chapters, runnable code, and numbers you can reproduce.
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About the Book
Data Voyage teaches production AI engineering through one project you build from start to finish: TalentLens, a job market intelligence platform. Over 24 chapters you collect real job postings, clean them, train and evaluate classifiers, build semantic search, add an LLM layer and a tool-calling agent, then ship the result as a tested FastAPI service in Docker, with a CI/CD pipeline and a published Python package.
Every chapter comes with runnable code, tests, and numbers measured on the bundled dataset, so what you read is what you get on your own machine. The book also shows where things go wrong: a leak that fakes a twenty-point gain in accuracy, a remote-pay gap that disappears once you control for seniority, and an agent that wastes half its tool calls.
It closes with four case studies from the author's own shipped products and open-source libraries, and a career playbook for the Indian AI and ML job market that keeps measured findings separate from judgement.
Who it is for: engineers and students who can already run scikit-learn examples and want to ship real systems.
What you get: a 377-page typeset PDF and an EPUB for e-readers and phones, to read offline. Every code reference opens the exact file on GitHub.
Why the code is open: a book about shipping real systems should let you check that every number reproduces. So the code is MIT-licensed, and the chapter text can be read at github.com/irfanalidv/Book-Data_Voyage. If the book helps you, buying this edition is how you support the work.
Published by DataCortex IQ.
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Irfan Ali
Irfan Ali is an AI engineer in Bengaluru. He was Head of AI at Kuration AI in Hong Kong, where he built the research and reliability layers behind its AI features, and before that he started and led the AI engineering work at Luminous Power Technologies in India, reporting to the CTO.
Since December 2025 he has built his own products: StackSift (stacksift.in), an LLM pipeline that identifies which products a B2B company actually sells, with cited evidence, and one of the case studies in this book; Reflecta, a voice check-in app; and Godam, stock and billing software that an FMCG distributor in Nepal uses every day.
He has published twelve open-source Python libraries on PyPI and two peer-reviewed papers, and has a Master's in Data Science and AI from IISER Tirupati (2026). Website: datacortex.in · GitHub: github.com/irfanalidv
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