Machine Learning Systems

We'll be working on a platform made up of seventy-nine recipes together. It starts off as a simple task, printing a line, but by the last chapter it covers extraction, warehousing, containers, machine learning and incident response. You can't just throw away examples in your work, and you shouldn't be doing that in your examples either. You don't need to be an Airflow expert to get started. What you're really learning here isn't a tool. It's all about making sure work is repeatable, observable and safe to rerun.

Behind every breakthrough in Generative AI isn't magic—it's mathematics."​Ever wondered what actually happens inside a Large Language Model when it generates the next token? Beyond the hype and high-level API calls lies an elegant foundation of probability, vector geometry, and linear algebra.​In The Mathematics of Generative AI, Ahmed Adawy strips away the academic jargon to reveal the exact core equations driving modern LLMs. From conditional probability and high-dimensional embeddings to Query-Key-Value attention mechanisms, this capsule equips software engineers and AI practitioners with the intuition needed to build, debug, and innovate with confidence.​Stop treating AI like a black box. Master the equations beneath the intelligence.

Forecasting is changing fast. This practical guide takes you from ARIMA and exponential smoothing to Transformers, PatchTST and foundation models like Chronos and TimesFM. With clear explanations, hands-on Python examples and an honest look at what works and what fails, you’ll learn how to build forecasting systems that hold up in the real world.

The Solana MEV Playbook: Building High-Frequency Arbitrage and MEV Bots in Rust