The Databricks platform and data-engineering playbook for the engineers who own pipelines, govern catalogs, and keep workloads on schedule. Sixteen chapters on Unity Catalog, Lakeflow, identity, observability, and performance. Azure examples; concepts mapped to AWS and GCP.

HNSW powers almost every vector database, and few of the engineers who rely on it have seen inside it. Vector Search Inside Out builds it from first principles in 92 step-by-step figures, then tunes it on 1.18 million real vectors. You'll know exactly what M, efConstruction and ef do, and how to set them for your own data.

Every sentence an AI writes can carry a hidden signature, and the world's biggest models are already signing their output. AI Watermarking shows you exactly how it works, using a four-word vocabulary and arithmetic you can check with a pocket calculator. By the end you will know what a watermark can prove, what destroys it in seconds, and why short text can never be tested.

Most asyncio tutorials introduce async and await on page one and ask you to take the runtime on faith. This book does it the other way around. You build a working event loop inthirty lines of plain Python — no asyncio import — and meet the keywords as labels for parts of a runtime you have already watched run. By the end, async Python stops beingintimidating and starts being readable.

Big O is not about seconds. It is about what happens to your work when your input gets ten times bigger, and that is the question interviewers are really asking. This book builds the notation from a list of names you can watch being searched, then hands you twenty everyday jobs in ten pairs and asks you to name the shape of each one.

A machine can hand you a grouping that is confident, stable, internally consistent, and seven times worse than the one it found a moment earlier. Nothing in the output says so.This book works k-means through completely on twelve bakery customers, small enough that every number is printed and checkable. You will build the method from nothing, see exactly where it succeeds, and see exactly where it fails silently.Fifty-eight figures, every one captured from a running implementation.

Four volumes. 76 chapters. 2,000+ pages. The complete data-engineering arc from your first spark.read.csv to a production multi-agent system on Databricks, written for the engineer who gets paged when the pipeline breaks at 2 a.m.

Most LLM books pick a side. Either they explain the math without showing the code, or they show the code without explaining the math. This is both. Two volumes. Eighteen chapters. From your first dot product in Chapter 5 to your fourth fine-tuning project in Chapter 17. Volume I builds the transformer from scratch. Volume II takes it into production. Together they are the only LLM set that walks the full arc from "what is attention" to "ship a fine-tuned model on a budget." If you have ever found a prompt trick that just worked and wished you knew why, or stared at a fine-tuning bill and wondered if you were doing it wrong, this is the set.

Learn Python asyncio from zero by building the event loop by hand. Six fetches go from 12 seconds to 3 on one thread, and you understand every line. Most asyncio tutorials throw async and await at you on page one. This course builds the event loop by hand first, in plain Python, so the keywords finally click. Six fetches drop from 12 seconds to 3, on one thread, and you understand every line. No magic. This interactive course has a companion book, Asyncio Without the Magic, on Leanpub at https://leanpub.com/asyncio . The book is the deep, cover-to-cover read; the course is where you make it stick with quizzes, build-it-yourself exercises, and runnable notebooks. They are built to be used together: read the idea in the book, then drill it in the course until it is yours. You get a lot from either one on its own, and the most by far from both together.

ChatGPT's memory. AI search. RAG. Recommendation engines. All of it stands on ONE idea — and almost nobody using AI actually understands it. This course takes you from absolute zero (no code, no math, no fees) to building your own AI search engine that finds things by meaning. In the age of AI, this is the line between people who prompt and people who build. Which side are you on?