Bundles

Build the skills needed to design, automate, secure, and operate modern infrastructure with this complete cybersecurity and systems engineering bundle. From creating custom Linux distributions and managing Kubernetes clusters to mastering automation, databases, and cloud-native workflows, these books provide practical knowledge and real-world techniques for engineers, administrators, and security professionals who want to take control of today’s complex technology environments.

Four books. One complete arc: master C++, debug like a forensic investigator, build an LLM inference engine from scratch, and understand quantization down to the bit level. Everything in one place, for engineers who want to understand — not just use.

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.

This GenAI bundle is perfect for ML engineers, AI product developers, and architects designing next-generation intelligent applications.

Aprende Python geoespacial desde cero hasta la GeoAI avanzada. Construye flujos de trabajo reales para SIG, gestión de datos espaciales y análisis impulsado por inteligencia artificial.

Apprenez Python pour le géospatial, des bases jusqu’à la GeoAI avancée. Développez des workflows concrets pour les SIG, la gestion des données spatiales et l’analyse basée sur l’IA.

We build solid backend services in Python, then add TypeScript to keep integrations and tooling reliable. Our APIs become easier to ship and harder to break.

Turn API testing into a repeatable system, not a ritual. Build collections, automate checks, and ship with confidence.

Query it in SQL, then sculpt it in pandas. Our data workflow becomes clean, fast, and analysis-ready.

We use JAX to write high-performance numerical code and pair it with GPU programming habits that unlock serious speed. We also reinforce applied AI patterns so our projects stay structured while they scale up.

We go from Django fundamentals to production-minded backend patterns that we can reuse across projects. We also keep momentum with practical recipes that turn common backend tasks into repeatable wins.

We standardize API testing with Postman workflows that make collaboration and regression checks painless. We also add Bruno so our collections can live cleanly in Git and fit neatly into CI.

We connect neural network fundamentals to modern PyTorch workflows so our models stop feeling like magic and start feeling like engineering. We then move faster with cookbook-style patterns for training loops, debugging, and experiments.

We build scalable AI pipelines with Spark and add Dask where Python-first parallelism fits best. We then instrument everything with OpenTelemetry so distributed systems become observable, measurable, and debuggable.