- 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 2026-10-01
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 case studies from three production systems 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. The companion code is open source at github.com/irfanalidv/Book-Data_Voyage, where you can also read the text for free. This edition gives you a typeset PDF and EPUB to read offline, with free updates, and buying it supports the work.
Published by DataCortex IQ.
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Irfan Ali
Irfan Ali is a senior AI engineer with seven years of production experience and the founder of DataCortex IQ. He has fine-tuned LLMs at a Schneider Electric subsidiary, built the AI layer for a Hong Kong startup, shipped a voice-first wellness app, and built inventory software for a Nepal-based FMCG business.
He has published eleven open-source libraries on PyPI and two peer-reviewed papers, and holds a Master's in Data Science and AI from IISER Tirupati. He writes from Siliguri, West Bengal. GitHub: github.com/irfanalidv
He also built StackSift (stacksift.in), an LLM pipeline and API that identifies which software products a B2B company actually sells, with cited evidence; it is one of the case studies in Data Voyage.
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