دانلود کتاب تسلط بر معماری و راهکارهای یادگیری ماشین از طراحی تا استقرار

In this book, we guide you in transforming abstract machine learning models into high-impact, robust, and scalable production systems. It is specifically designed for data scientists, ML engineers, and MLOps practitioners looking to master the entire lifecycle. The main challenge in MLOps is bridging the gap between reliable production operation and model building. This book offers a structured approach to master these advanced skills across software engineering, specialized ML techniques, and DevOps. Here, we cover the architecture of ML solutions across the end-to-end deployment lifecycle, including:
Infrastructure and Deployment: Mastering containerization (Docker) and orchestration (Kubernetes).
Quality Assurance: Designing and implementing tests for data, features, and model performance.
Model Management: Techniques for versioning, automated retraining, and various drift handling.
Responsible AI: Ensuring model fairness, security, and transparency.
Advanced Topics: Exploring cutting-edge trends like agentic AI systems, transformers, and edge AI.
To maximize learning, each chapter follows a specific approach including theory, practical code snippets, and a real-world step-by-step project. I recommend running all code samples by yourself for translating your theoretical knowledge into practical MLOps skills.