Managing and Monitoring Foundation Models on AWS

As foundation models become central to modern software, deploying them is only the first step; keeping them reliable, cost-effective, and accurate in production is the real challenge. This text-based course guides you through the essentials of managing, monitoring, and orchestrating large-scale AI models using AWS. You will transition from manual model deployment to building robust, automated MLOps pipelines while learning how to track model performance, detect data drift, and manage multi-task AI systems effectively. What you'll learn: - Understand foundational concepts of MLOps, LLMOps, and the lifecycle of foundation models in production. - Deploy foundation models on AWS SageMaker using scalable and cost-effective hosting patterns. - Monitor model performance, latency, and resource utilization to ensure high availability. - Detect data drift and model degradation using modern observability workflows. - Build automated MLOps pipelines to streamline retraining, evaluation, and deployment. - Implement security best practices and guardrails for multi-task AI applications. The course starts with essential terminology and foundational definitions of foundation models and MLOps. From there, you will progress through step-by-step written explanations covering deployment, real-time monitoring, drift detection, and pipeline automation on AWS. This course is designed for beginners in AI engineering, software developers, and aspiring DevOps professionals, with no prior experience in machine learning operations required. Start reading today to master the operational side of modern artificial intelligence.