Foundation Model Engineering: Master Data Preparation, Model Training, Fine-Tuning, Evaluation, and Scalable Deployment - Softcover

Inhaltsangabe

The future of artificial intelligence is being built on foundation models—and the engineers who understand how they work will shape the next generation of intelligent systems.

Whether you're building large language models, training domain-specific AI, fine-tuning open-source models, or deploying production-ready inference systems, Foundation Model Engineering provides the practical knowledge needed to move beyond theory and into real-world implementation.

Unlike books that focus only on machine learning concepts or isolated model architectures, this book follows the complete engineering lifecycle of modern foundation models—from collecting and curating massive datasets to training, fine-tuning, evaluating, optimizing, and deploying models that can reliably serve millions of users.

Inside, you'll learn how modern foundation models are actually built and maintained in production environments. You'll discover how to engineer scalable data pipelines, clean and deduplicate web-scale datasets, train efficient tokenizers, understand transformer architectures, perform domain adaptation, implement parameter-efficient fine-tuning techniques such as LoRA, align models for instruction following, evaluate real-world performance, optimize inference with quantization and caching, and design reliable AI systems that balance capability, latency, and cost.

Rather than overwhelming you with abstract research or mathematical proofs, this book emphasizes engineering decisions, practical trade-offs, production workflows, and proven implementation strategies used throughout today's AI industry. Every major concept is supported with clear explanations, practical examples, and production-oriented Python code designed to help you build intuition while developing real engineering skills.

• Understand the evolution from traditional machine learning to foundation models.
• Master transformer architecture from an engineering perspective.
• Build high-quality datasets for large-scale model pretraining.
• Clean, filter, deduplicate, and tokenize massive text corpora.
• Design scalable data pipelines for distributed training.
• Understand foundation model pretraining and domain adaptation.
• Implement parameter-efficient fine-tuning with LoRA and modern PEFT methods.
• Apply instruction tuning, alignment, and preference optimization.
• Evaluate foundation models using practical benchmarking techniques.
• Optimize inference with quantization, batching, KV cache, and efficient serving.
• Deploy, monitor, and scale foundation models in production environments.

• Machine Learning Engineers transitioning into foundation model development.
• AI Engineers building LLM-powered products and services.
• Data Scientists seeking a deeper understanding of modern language models.
• Software Engineers moving into Generative AI.
• MLOps Engineers responsible for deploying and scaling AI systems.
• Researchers looking for a practical engineering perspective.
• Graduate students and advanced learners preparing for careers in Artificial Intelligence.

Whether you're working with open-source models such as Llama, Mistral, Qwen, DeepSeek, Gemma, or other transformer-based architectures, the engineering principles presented in this book will help you build AI systems that are efficient, scalable, reliable, and ready for production.

Foundation models have fundamentally changed artificial intelligence. Success is no longer determined by understanding only the model itself—it depends on mastering the complete engineering ecosystem surrounding it.

If you're ready to move beyond using foundation models and start engineering them, this book will become an essential resource you'll return to throughout your AI career.

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