Books

Did you know that recursion is the same as induction? That you have an inductive hypothesis and that you're allowed to assume n-1 and AHHHHHHHHHHHHHHHHHHHHH Stop it. I don't care about that right now. I care about teaching you recursion and not scaring you off. We are going to do this using Python and the best examples and explanations that I know.

Understanding the most common mistakes in machine learning will allow you not only to avoid them, but to build better machine learning systems and less prone to errors. After reading this book, you will be ready to build more robust and trustworthy machine learning models.

Learn to Think Like a Senior Engineer — by Building a Card Game EngineA hands-on guide to system design, clean architecture, and engineering judgment. No fluff. Just practical, test-driven code you’ll actually use.

Ever wished someone would explain coding like they’re talking to a friend?That’s exactly what Python – Made Simpler does. This beginner-friendly book teaches Python with the help of real-life analogies you already understand. Variables become jars in your kitchen. Loops feel like chores on repeat. And instead of just reading about code, you’ll actually write it—from day one. You don’t need any prior experience. You don’t need to be a tech wizard. You just need curiosity—and maybe a little love for coffee, because we talk about that too. Start learning Python the fun way—with simplicity, creativity, and confidence.

Python is awesome, Kubernetes is awesome too. Use the combined power of both to create highly available Python application, declarative data processing task, and all sort of automation tasks.

Cheat sheets can be a really helpful resource when you're learning a new language. They remind you of the syntax and concepts you've just learned, so you can focus on writing your own code instead of rereading sections of a book or rewatching parts of a video.

A breezy guide to using Python's built-in support for logging. The guide covers general concepts relevant to logging, logging APIs in Python and how to use them, and good practices, gotchas, and performance considerations associated with these APIs.

Apprendre à programmer n'a jamais été aussi fun !

RAG, Agent Bricks, the Multi-Agent Supervisor with MCP, Lakebase, MLflow 3, Lakehouse Monitoring, Feature Store, Vector Search. Every AI surface Databricks shipped at GA in 2025 and 2026, taught by a practitioner, current to 2026. What you will learn - Build RAG pipelines with Vector Search, embedding models, and citation grounding- Ship Agent Bricks for classification and information extraction- Orchestrate specialist agents with the Multi-Agent Supervisor and MCP- Use Lakebase as the operational Postgres layer for AI apps and agents- Detect data and model drift with Lakehouse Monitoring; wire alerts to retraining- Manage the ML lifecycle with MLflow 3 and the UC Model Registry- Govern features across training and serving with Feature Store (offline + online)- Serve foundation and custom models with AI Gateway controls Who this book is for Data engineers, ML engineers, and AI/ML architects who know PySpark and the Databricks platform and now need to ship production AI. Volume 3 is the recommended prerequisite. Table of Contents 1. Databricks SQL in Production. Warehouses, materialized views, three latency signals (admission, compilation, execution), the full dashboard backend wiring.2. External BI: Tableau, Power BI, dbt. Performance tips that take a dashboard from sluggish to instant, dbt configuration at incremental scale, the seam between BI and the lakehouse.3. AI/BI Dashboards. Anatomy of a Lakeview dashboard, draft vs published flow, the Dashboard Agent's reliable patterns, the five-grant permission model.4. Genie: Natural-Language Analytics. Grounding sources, the priority rule, the SQL Genie actually writes, the questions Genie answers cleanly versus the ones that confuse it.5. AI SQL Functions. ai_query, ai_parse_document, ai_extract for PDFs and HTML, univariate forecasts, the daily cost math for production AI SQL pipelines.6. Model Serving. Endpoints, the three fields that decide capacity and cost, the chat-completion payload, the five moving pieces of a production recommender.7. Foundation Models. Five major providers, the External Models config, the vendor-swap pattern (Claude to Gemini in hours, not weeks), the three habits that keep swap cost low.8. Vector Search and RAG. Six delta-sync arguments, three chunking strategies compared, the RAG function your app imports, end-to-end answer evaluation with traces.9. MLflow 3 and UC Model Registry. Versions, aliases, tags (and what each is not for), five tracking calls and what each one writes, the experiment-to-production lifecycle.10. Feature Store. Why SDP is the right producer, the six-file project layout, four parity-failure classes between offline and online stores and what causes each.11. MLOps as a Practice. Seven sources every incident reads from, three deploy patterns (canary, shadow, blue-green), three retrain strategies, five golden signals for an ML endpoint.12. Lakehouse Monitoring: Drift Detection. Six monitor parameters, the loop from drift alert to retraining, what to do when the baseline table is missing.13. Distributed Deep Learning. Three signals that force distributed training, picking the flavor (data, model, hybrid) from the bottleneck, four pieces of GPU memory worked out for a 7B model.14. Agent Bricks. Declarative classification and information-extraction agents, eval-set ingredients, the pre-compute pattern that makes small seed sets work.15. Multi-Agent Supervisor and MCP. The supervisor build, synthetic-turn evaluation, three real conversations end to end, the auth-passthrough chain across child agents.16. Lakebase: Operational Postgres for AI. Five alternatives compared, sub-10ms reads for AI apps, the lineage from Delta source through SDP into Postgres and onward to the endpoint.17. Capstone: Retail Intelligence App. Ten stages, each anchored to an earlier chapter. The smoke test that confirms every stage of the platform is reachable, the new-data path through the recommender.18. Certification and What's Next. The certification paths that actually map to the book, and the reading list the on-call team uses when something breaks.

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.

Structured Streaming, MLlib, GraphFrames, performance tuning, testing and CI, and the lakehouse. Eleven chapters that take a competent PySpark user from "the job runs" to "the on-call team trusts the job.

PySpark from page one. Ten chapters that take a Python user who knows pandas and turn them into someone who can write, read, and debug production PySpark, without a three-chapter detour through distributed-computing theory.

A hands-on guide to downloading, running, serving, and maintaining open-weight LLMs on your own machine (489 manuscript pages).