Enterprise-scale deep learning demands more than a single GPU; it requires distributed infrastructure, optimized training pipelines, and the ability to build advanced multi-domain applications. This course builds the engineering skills to scale deep learning workflows and deploy production-grade models on Azure ML.

You'll implement PyTorch Distributed Data Parallel (DDP) and Fully Sharded Data Parallel (FSDP) pipelines, profiling communication overhead and comparing throughput across single- and multi-node configurations. You'll configure DeepSpeed ZeRO optimization stages, including CPU offloading, and apply Microsoft's full training acceleration stack using Microsoft Olive and Azure Container for PyTorch. You'll build object detection and vision-language pipelines using Florence-2 and CLIP, and engineer enterprise NLP workflows for classification, NER, and extractive QA with Hugging Face Transformers and Azure AI Language. You'll also design multimodal fusion architectures combining vision, text, and audio using Microsoft Phi-4 and OpenAI Whisper. By the end of this course, you'll be able to implement distributed training at scale, accelerate large model training with Microsoft's optimization stack, and build production-grade pipelines across computer vision, NLP, and multimodal domains. This course is designed for advanced machine learning engineers scaling infrastructure for massive datasets and multi-modal application development.