Overview

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Production-grade deep learning starts with solid engineering foundations. This course builds the core PyTorch and Azure ML skills needed to build, train, and track deep learning models in enterprise environments. You'll implement feedforward neural networks from scratch, including forward and backward passes, loss functions, and optimizers, then move into custom PyTorch module design, efficient DataLoader pipelines, and mixed precision training with torch.compile. You'll configure Azure ML workspaces, compute clusters, and GPU targets using the Azure ML SDK v2, manage training jobs, and track experiments with MLflow. By the end of this course, you'll be able to build custom PyTorch training pipelines, configure Azure ML environments, and monitor deep learning workflows from end to end. This course is designed for engineers with intermediate Python and hands-on ML experience, ready to build deep learning skills on Azure. Labs use a dual-path design: a gradable track with precomputed outputs and logs supports workflow mastery without live GPU provisioning, while an optional live path is available for enterprise learners with Azure subscriptions and GPU quotas.

Syllabus

Neural Network Foundations: Feedforward Networks
- Establish a robust understanding of core deep learning architecture. You will explore and manually code computational graphs, evaluating how data moves through forward and backward passes.
Neural Network Foundations: Loss & Optimizers
- Move beyond architecture setup and focus on convergence. You will configure and test advanced optimization and activation techniques to evaluate their direct impact on model performance.
PyTorch Deep Dive: Custom Modules & Autograd
- Move beyond pre-packaged solutions to establish modular control over your network components. You will structure custom nn.Module classes and explicitly interact with PyTorch's autograd engine.
PyTorch Deep Dive: DataLoaders & Compilation
- Tackle performance bottlenecks at the source. You will build data ingestion pipelines and implement PyTorch's native acceleration tools to train networks significantly faster.
Azure ML: Workspaces, Compute and Environments
- Establish your deep learning infrastructure in the cloud. You will learn to programmatically define and provision Azure ML workspaces, map scalable modern NCasT4-series GPU compute targets and secure your execution environments using production-ready container registries.
Azure ML: Jobs, Data Assets and MLflow Experiment Tracking
- Execute and track your deep learning training pipelines. You will format PyTorch training scripts as Azure ML command jobs, connect MLflow for seamless metric tracking, and securely map data assets.
Project Module: Deep Learning Environment & Baseline Model
- Synthesize your foundational engineering skills to provision a cloud environment and train a baseline neural network. You will design an end-to-end blueprint that integrates custom PyTorch modules, optimized DataLoaders, and Azure ML scaling configurations.