ML Model Optimization: Quantization, Pruning, and Production Design

Deploying machine learning models to production requires more than just high accuracy; it demands speed, efficiency, and cost-effectiveness. Many developers struggle with slow inference times and rising cloud infrastructure costs when scaling their systems. This text-based course guides you from foundational machine learning deployment concepts to the optimization strategies used in modern production environments. You will learn how to shrink model sizes and accelerate execution speeds without sacrificing predictive performance, preparing you to design highly efficient systems. What you'll learn: Understand the core trade-offs between model size, inference latency, and accuracy; Apply quantization techniques to convert weights to lower-bit formats; Implement model pruning to remove redundant parameters and streamline network architecture; Explore knowledge distillation to transfer capabilities from large models to lightweight alternatives; Configure modern compilation tools and runtimes like ONNX to optimize execution across hardware; Analyze production metrics to measure throughput, latency, and resource utilization. The course begins with essential terminology and the mathematical foundations of model size, then progresses through step-by-step written explanations of pruning, quantization, and compilation. Designed for software engineers, aspiring ML engineers, and data scientists who are new to system design, this course requires no advanced systems programming experience. Start reading today to build faster, cheaper, and more efficient machine learning systems.