The Ultimate Agentic AI & LLM Production Mastery Bundle

Master the Transition from AI Prototypes to Production-Grade Systems

Moving artificial intelligence and generative models from experimental Jupyter notebooks to scalable, reliable production environments requires robust systems engineering. This book is a practical, hands-on guide for software engineers, machine learning practitioners, and technical architects building end-to-end AI infrastructure.

What You Will Learn:

• Foundations & Architecture: Deep dive into LLM architectures, Generative AI mechanisms, and core Machine Learning principles.

• Performance Optimization: Eliminate computational bottlenecks, optimize pure Python execution, and leverage high-performance vectorized operations.

• Production Deployment: Build secure, scalable containerized microservices and backend architectures for AI workloads.

• Real-World Implementation: Construct robust AI pipelines using modern Python frameworks and production best practices.

Whether you are an engineer looking to scale your AI prototypes or an architect designing enterprise systems, this book bridges the gap between machine learning concepts and production-grade deployment.

About the Book

Running an LLM is one problem. Understanding its memory footprint, execution phases, and serving behavior is another.

LLM Inference Engine Architecture explores the engineering behind language-model inference—from prefill and decode to KV-cache management, attention kernels, quantization, and request scheduling.

Written for software engineers and ML engineers with a working knowledge of Python and transformer fundamentals, the book connects mathematical explanations with implementation examples and serving-system design considerations.

What You Will Explore

• Inference fundamentals: Trace the request lifecycle and understand the different computational characteristics of prefill and decode.
• Memory budgeting: Work through model-weight and KV-cache estimates, including the effects of context length, KV-head count, precision, and concurrent sequences.
• Attention and memory management: Explore PagedAttention, non-contiguous KV allocation, and the relationship between attention computation and memory access.
• Quantization: Examine weight and KV-cache precision choices, including FP8 and low-bit weight-quantization approaches.
• Serving architecture: Study continuous batching, request scheduling, multi-GPU execution, and latency and throughput metrics.
• Kernel development: Explore CUDA and Triton concepts, kernel fusion, and a quantized matrix-multiplication case study.

The book includes numerical examples, reference code, and an educational asynchronous serving simulator. These examples are intended to explain mechanisms and trade-offs—not to replace hardware-specific profiling or production validation.

Read the sample to review the mathematical level, coding style, and scope before purchasing.

Modern AI systems retrieve information by meaning rather than exact keyword matches. Under the hood, this capability relies on a surprisingly simple foundation: representing text as vectors, measuring similarity, and ranking results.

​In Semantic Search from Scratch, you will build this core mechanism yourself from first principles using only Python and NumPy.

​We deliberately avoid third-party vector databases, machine-learning frameworks, and high-level retrieval libraries. The goal isn't to build a production platform, but to demystify the mathematical mechanics behind semantic retrieval, vector spaces, and Retrieval-Augmented Generation (RAG).

​What You Will Learn & Build:

• ​Vectors as Representations: Understand how natural language transforms into dense numerical representations.
• ​Mathematical Similarity: Implement Cosine Similarity from scratch using raw matrix operations.
• ​Document Indexing: Construct a complete educational vector index to store and query text representations.
• ​Top-K Retrieval & Ranking: Build a scoring loop that ranks contextually relevant documents for downstream AI prompts.
• ​RAG Foundations: Connect the dots between vector similarity and modern AI retrieval pipelines.

​Who Is This Capsule For?

​This guide is designed for intermediate Python developers, AI engineers, and curiosity-driven builders who want to peel back the layers of high-level AI libraries and truly understand how semantic search works under the hood.

Python Testing with pytest is a practical guide to building reliable Python software through automated testing.

Writing code that works is only the beginning. As projects grow, refactoring, new features, and changing requirements can introduce unexpected regressions. A well-designed test suite provides the safety net developers need to change their code with confidence.

This capsule introduces pytest from the ground up and gradually moves toward the practices used in real-world Python projects.

You will learn how to write clear and focused tests, use assertions effectively, test normal and edge-case behavior, verify exceptions, organize test code, understand pytest output, and build a repeatable testing workflow.

The book also introduces the principles behind Arrange–Act–Assert, reusable testing patterns, parametrization, fixtures, test organization, code coverage, continuous integration, and professional testing practices.

Rather than treating testing as an afterthought, this book presents it as an essential part of software development.

Whether you are a Python developer, a student learning software engineering, or a developer moving from small scripts toward larger applications, this capsule gives you a practical foundation for writing software that is easier to maintain, refactor, and trust.

Learn the tools. Understand the principles. Build with confidence.

Ahmed Adawy Tech Capsules
Practical engineering knowledge, one focused capsule at a time.