// DESIGNING SCALABLE AI SYSTEMS

Dive into the core principles of AI system design, understanding what makes AI applications unique and how to lay a solid foundation for scalable, …

Explore the foundational concepts of AI/ML pipelines, from data ingestion and preparation to model training, deployment, and continuous monitoring, …

Dive into microservices for AI, learning how to design modular, scalable, and resilient AI-powered applications. Explore patterns for integrating ML …

Learn how to design robust, scalable, and secure APIs for AI-powered applications, covering integration patterns, communication protocols, and best …

Explore Event-Driven Architectures (EDA) for AI systems. Learn how to design scalable, real-time, and resilient AI applications using events, message …

Learn how to design and implement robust orchestration for complex AI workflows and multi-agent systems, enhancing scalability and reliability.

Explore Distributed AI architectures for scaling model training and inference. Learn about data and model parallelism, horizontal scaling, and fault …

Explore the critical concepts of data quality, model trustworthiness, and responsible AI principles for designing robust, scalable, and ethical AI …

Master observability for AI systems: understand monitoring, structured logging, distributed tracing, and ML-specific metrics to build robust, …

Explore the critical aspects of designing secure, privacy-preserving, and ethically responsible AI systems for production environments. Learn about …

Learn to design a scalable, real-time recommendation engine using microservices, event-driven architecture, and distributed AI principles with …

Explore the evolution of AI architectures, focusing on Large Language Models (LLMs), Generative AI, and AI Agents. Learn patterns like RAG, …