Scaling deep learning in production requires more than working code; it requires systematic tuning, efficient pipelines, and the ability to diagnose failures before deployment. This course builds operational skills to manage deep learning workflows at enterprise scale on Azure ML.

You'll implement LoRA and QLoRA fine-tuning for large language models using Hugging Face PEFT, comparing memory use, training throughput, and performance. You'll design hyperparameter optimization experiments using Azure ML sweep jobs with Bayesian sampling and early termination, tracking runs with MLflow. You'll diagnose failures such as vanishing gradients, overfitting, and normalization issues using PyTorch Profiler and ablation studies. You'll also build high-throughput data pipelines with WebDataset, LMDB, and Azure ML Data Assets, profiling I/O bottlenecks to maximize GPU utilization. By the end of this course, you'll be able to apply parameter-efficient fine-tuning, run systematic searches, debug failures, and design scalable data pipelines. This course is designed for deep learning operations engineers focused on optimization, debugging, and memory-efficient fine-tuning.