Production ML models failing your latency targets? Learn how to make them run 3-5x faster without losing accuracy. This course helps ML engineers and data scientists optimize neural network inference for real-world deployment—across mobile, edge, and cloud environments. If you face slow model inference, high infrastructure costs, or deployment constraints, this course provides practical solutions. You'll master profiling techniques to identify performance bottlenecks, apply quantization to cut precision requirements, and make smart trade-offs between speed, accuracy, and resource constraints. You'll learn to benchmark optimization techniques and select the right approach for deployment scenarios. You'll explore inference profiling and metrics, pruning strategies, and quantization methods. You'll practice with real-world cases—from streaming platforms to autonomous vehicles—using industry-standard tools like PyTorch Profiler, TensorRT, and pruning utilities.

This course is ideal for machine learning engineers, data scientists, and AI practitioners who are deploying or optimizing models in production. It’s also valuable for MLOps professionals and system engineers responsible for performance tuning in resource-constrained environments (e.g., mobile, embedded, or cloud inference systems). Learners should have a good grasp of Python and basic experience with PyTorch or TensorFlow. Familiarity with machine learning concepts, such as model training and evaluation, is expected. Understanding how neural networks work and basic performance metrics like latency and accuracy will help you get the most from this course. By the end of this course, you’ll confidently optimize production models, cut inference costs, meet latency goals, and deploy ML systems that scale efficiently.