Production Machine Learning Systems: Designing Scalable Cloud ML

Moving a machine learning model from a local environment to a production-ready system requires a shift in mindset from simple prediction to robust system architecture. This text-based course guides you through the essential principles of designing, deploying, and maintaining high-performance ML systems on cloud platforms. You will transition from writing basic model code to understanding how entire ML pipelines operate at scale, preparing you to collaborate effectively on modern cloud infrastructure. What you'll learn: - Understand the core differences between static and dynamic training and inference strategies. - Explore distributed training architectures using TensorFlow and specialized hardware like TPUs. - Design automated pipelines that handle data ingestion, model training, and continuous deployment. - Apply modern MLOps best practices for model monitoring, drift detection, and performance tracking. - Configure scalable serving infrastructure to handle real-time and batch predictions. The course begins with foundational definitions of production ML systems, then guides you step-by-step through training paradigms, distributed computing, and live model monitoring. Designed for aspiring ML engineers, data scientists, and developers new to cloud deployment, this course requires no prior production experience and begins with fundamental concepts. Start reading today to build reliable, production-grade machine learning systems.