Books

Evaluation Engineering for AI Systems is a practical guide to building reliable AI evaluations for production. Learn how to measure performance, compare models, detect regressions and create evaluation pipelines using real-world examples, modern Python and proven industry practices.

AI can generate code in seconds, but shipping production-grade software requires far more than fast prompts. Vibe Engineering shows you how to work with AI coding assistants to build systems that are reliable, secure, and maintainable. Through practical examples and proven workflows, you will learn how to turn AI from a code generator into a trusted engineering partner.

Run Jev-style decision models on your own hardware with Ollaya. Learn how these fast, structured models differ from LLMs, then build real applications with typed outputs, Python, JavaScript and MCP. From first setup to production, this practical guide shows you how to make AI faster, cheaper and more private.

AI agents are moving from experiments into real production systems. This book shows experienced engineers how to design, deploy and operate reliable agent systems that can scale. From architecture and infrastructure to security, reliability and governance, it provides practical patterns and production-ready examples for building autonomous systems that work in the real world.

Modern Deep Learning models can be extremely large, often exceeding the memory capacity of a single GPU or CPU. In these cases, training must be distributed across multiple processors. This introduces the need for high-speed communication between GPUs—both within a single server and across multiple servers. Intra-node GPU communication typically relies on high-speed interconnects like NVLink, with Direct Memory Access operations enabling efficient data transfers between GPUs. Inter-node communication, however, depends on the backend network, either  InfiniBand or Ethernet-based. Synchronization of model parameters across GPUs places strict requirements on the network: high throughput, ultra-low latency, and zero packet loss. Achieving this in an Ethernet fabric is challenging but possible.   This is where datacenter networking meets Deep Learning. Understanding how GPUs communicate and what the network must deliver is essential for designing effective AI data center infrastructures.

Chunk at 500 with 50 of overlap. Retrieve five. Add a reranker. Use hybrid search.Repeated everywhere, justified almost nowhere. This book measures them instead, on corpora built so that the correct answer follows fromhow they were made. Several results contradicted the advice: the hybrid combination lost inevery condition tested, neither reranker produced an effect above the noise floor, andattribution collapsed to 0.170 while retrieval was still reporting 0.920.

The essentials of making predictions using supervised regression and classification for tabular data. Tech stack: python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, TabICL

LTX 2.3 is a powerful AI video model, but great results depend on great prompts. This guide teaches you how to create consistent, cinematic videos with practical techniques, proven prompt patterns, and production-ready workflows.

A chatbot can describe how to do your work. An agent can do it—if you build it to be trusted. Learn to construct, verify, and deploy AI agents that earn a place in real scientific and engineering practice.

Build a complete data pipeline from scratch — no cloud signup needed. Fifteen chapters covering ingestion, loading, data modeling, dbt, Airflow, data quality, streaming, observability, and performance optimization. Everything runs locally with Python and DuckDB.

Teams are producing more code than ever. Dashboards are green. And something is quietly breaking. AI coding assistants didn't break your development process; they revealed it was already fragile. Reimagine, Don't Retrofit is a field-tested argument that the software development lifecycle itself needs to be reimagined for the AI era, from governance and metrics to team roles and delivery flow.

Become a better data scientist by understanding different modeling mindsets.

Learn Claude Code by building real projects. This hands-on companion turns the Claude Code Masterclass workshop into a practical self-paced guide for planning, coding, testing, reviewing, refactoring, and shipping software with AI.