Speed Up AI: Optimizing and Benchmarking Machine Learning Models

Slow AI models can bottleneck entire applications and drive up computing costs. Understanding how to measure and improve the speed of your algorithms is essential for building practical, production-ready systems. This text-only course guides you through the fundamental principles of AI performance analysis, showing you how to identify bottlenecks and apply optimization strategies. You will transition from writing unoptimized code to understanding how to deploy streamlined, high-performance machine learning models. What you'll learn: - Understand foundational performance metrics, latency, throughput, and hardware constraints. - Profile AI code using standard profiling tools to locate execution bottlenecks. - Apply optimization techniques such as model quantization, pruning, and structural simplification. - Benchmark models systematically to ensure reliable, reproducible speed measurements. - Leverage modern runtime engines like ONNX to run models efficiently across different environments. You will start with core concepts of hardware utilization and performance terminology before moving into written walkthroughs of profiling and optimization techniques. Through clear explanations and code-based exercises, you will learn to systematically accelerate model execution. This course is designed for beginner software developers, data scientists, and AI enthusiasts who want to improve model efficiency. No prior experience with hardware optimization is required. Start reading today to unlock the full speed potential of your machine learning models.