Cloud-Native AI - Architectural Patterns for Enterprise Success

Explore cloud-native AI architectural patterns for enterprise success in this 20-minute conference talk from Conf42 ML 2025. Learn the fundamental differences between traditional and cloud-native AI deployment approaches, discovering how containerization transforms AI application development and deployment. Master Infrastructure as Code (IAC) principles for scalable AI systems and understand layered architectural patterns that support enterprise-grade solutions. Examine the distinctions between training and inference environments, and discover how feature stores enhance AI model management and data consistency. Dive into Kubernetes orchestration patterns specifically designed for AI workloads, including strategies for managing specialized hardware like GPUs and TPUs. Understand MLOps implementation through CI/CD pipelines that automate model deployment and versioning. Learn automated monitoring and maintenance techniques that ensure AI systems remain performant and reliable in production environments. Gain practical insights into building robust, scalable cloud-native AI architectures that meet enterprise requirements for reliability, security, and operational efficiency.