Machine Learning Model Optimization

This intermediate-level Nanodegree program equips learners with advanced skills and practical experience in optimizing machine learning models for performance, scalability, and real-world application. Students will explore foundational principles and techniques such as quantization, pruning, sparsity, and profiling, and apply these to both traditional machine learning models and large language models (LLMs). The program delves into advanced model compression methods, including low-rank compression and knowledge distillation, and covers the design of efficient architectures for hardware acceleration using tools like TensorRT and ONNX. Learners will also work on mobile and GPU deployments and LLM inference optimization. Ultimately, graduates will be able to optimize inference pipelines for LLMs and deploy efficient models to meet specific performance and deployment requirements.