Browse by subject.

Books on building, deploying, and operating AI and machine learning systems in production. From data pipelines to model serving.

Books on understanding, building, fine-tuning, and deploying large language models. From transformer internals to production LLM apps.

Books on crafting effective prompts, building LLM-powered applications, and getting reliable outputs from AI models.

Books covering classical machine learning, scikit-learn, and the foundations every developer needs before going deep on LLMs or deep learning.

Books on neural networks, CNNs, RNNs, transformers, and generative models. The architecture-level understanding behind modern AI.

Books on building agentic AI systems that can plan, reason, use tools, and operate autonomously. The 2026 frontier of AI engineering.

Books on how AI is reshaping work, business, and society. For developers who want context beyond the code.

Books on designing reliable, scalable, and maintainable backend systems. Storage, replication, consensus, streaming, and the trade-offs behind every architectural choice.

Books on the daily craft of writing software — clean code, refactoring, design principles, and the habits that separate working code from code that lasts.

Books on how teams actually build software over time — code review, testing, version control, dependency management, and the practices behind long-lived codebases.