Microsoft Deep Learning Engineering with Azure

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Enterprise Deep Learning requires more than model building. It demands production-grade pipelines, efficient large-scale training, and robust deployment operations. This program provides end-to-end skills to design, train, optimize, and deploy solutions using PyTorch and Azure Machine Learning. Build a portfolio of applied engineering work. Projects include training reports, optimization benchmarks, architecture decisions, and deployment runbooks from real-world deep learning scenarios. You will implement architectures like CNNs, RNNs, and Transformers; fine-tune large language and vision models using LoRA and QLoRA; manage experiments with MLflow and Azure ML sweep jobs; scale training with DeepSpeed ZeRO and FSDP; apply quantization, pruning, and knowledge distillation; accelerate inference with ONNX Runtime and TensorRT; and finally, deploy containerized models to Azure ML managed endpoints with blue/green patterns. By the end, you will engineer the full deep learning lifecycle—from setup through distributed training, compression, and deployment. Five courses build your expertise progressively. Hands-on labs use real-world scenarios and precomputed outputs, supporting full workflow mastery without requiring live GPU quota provisioning. This program is for machine learning practitioners, data scientists, and software engineers specializing in deep learning on Azure. You need intermediate Python, hands-on ML experience, and basic cloud familiarity before starting.

Syllabus

Course 1: Deep Learning Foundations & Azure Environments
Course 2: Core Neural Architectures & Generative Models
Course 3: Experiment Management, Tuning & Debugging
Course 4: Distributed Training & Advanced Application
Course 5: Model Optimization, Inference & End-to-End Engineering
Course 6: Launch Your Deep Learning Career

Courses

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Building production-ready deep learning systems requires fluency across multiple architecture families and modern MLOps tooling. This course covers core neural architectures used in modern AI engineering, from convolutional and sequence models to Transformers and generative systems, alongside the practical toolchain required to build, track, and evaluate them. To succeed in this course, learners should bring strong Python programming skills, a foundational understanding of machine learning, and hands-on familiarity with PyTorch. Hands-on labs and projects utilize TorchVision and Albumentations for image transformations, Hugging Face Diffusers for generative pipelines, and TorchMetrics for specialized evaluation (such as Fréchet Inception Distance). Model tracking, experimentation, and workflows are integrated using MLflow within a Databricks workspace environment. You'll implement CNN architectures, including ResNet and ConvNeXt with transfer learning, build LSTM and GRU models for time series and classification, engineer Transformer attention blocks, fine-tune BERT and ViT models using Hugging Face, and implement VAEs, GANs, and Diffusion pipelines using Hugging Face Diffusers. By the end of this course, you'll be able to select, implement, and evaluate neural architectures for computer vision, temporal, NLP, and generative tasks, and produce documentation supporting production architecture decisions. This course is designed for deep learning engineers specializing in computer vision, time series, and generative AI. 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.
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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.
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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.