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Microsoft Deep Learning Engineering with Azure Professional Certificate

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Microsoft Deep Learning Engineering with Azure Professional Certificate

Engineer Deep Learning Models on Azure.

Design, scale, optimize, and deploy deep learning architectures using PyTorch and Azure ML.

Instructor: Microsoft

Included with Learn more

6 course series

Earn a career credential that demonstrates your expertise

Beginner level

Recommended experience

Flexible schedule

Learn at your own pace

6 course series

Earn a career credential that demonstrates your expertise

Beginner level

Recommended experience

Flexible schedule

Learn at your own pace

What you'll learn

Design and train CNNs, RNNs, and Transformers for vision, sequence, and NLP tasks using PyTorch and Hugging Face.
Orchestrate experiment tracking, hyperparameter optimization, and scalable data pipelines using Azure ML and MLflow.
Apply Parameter-Efficient Fine-Tuning (PEFT) and distributed training with DeepSpeed and FSDP to scale large models.
Optimize and deploy deep learning models to Azure ML endpoints using quantization, pruning, and ONNX Runtime.

Details to know

Shareable certificate

Add to your LinkedIn profile

Taught in English

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Professional Certificate - 6 course series

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.

Applied Learning Project

Throughout this program, you'll complete hands-on projects that mirror real-world deep learning engineering scenarios. You'll build an end-to-end Azure ML training pipeline, fine-tune transformer models for domain-specific tasks using LoRA and QLoRA, scale large model training with DeepSpeed ZeRO optimization, and compress production-bound models using quantization, pruning, and ONNX Runtime. Projects combine PyTorch fundamentals with enterprise-grade Azure infrastructure, giving you a portfolio of documented engineering work, including architecture decisions, training reports, optimization benchmarks, and deployment runbooks, ready to share with employers.

Deep Learning Foundations & Azure Environments

Course 1, 9 hours

What you'll learn

Implement feedforward neural networks in PyTorch, including forward/backward passes, loss functions, and optimizer configuration
Build custom nn.Module classes and configure DataLoader pipelines with mixed precision training and torch.compile optimization
Configure Azure ML workspaces, compute clusters, and GPU targets using the Azure ML SDK v2
Submit training jobs, track experiments with MLflow, and register models using the Azure ML model registry

Core Neural Architectures & Generative Models

Course 2, 12 hours

What you'll learn

Design and train CNN architectures, including ResNet and ConvNeXt with transfer learning and data augmentation pipelines.
Implement LSTM, GRU, and Temporal Convolutional Networks for sequence classification and time series forecasting.
Engineer Transformer attention blocks from scratch and fine-tune BERT and ViT models using Hugging Face Transformers.
Implement and evaluate generative models including VAEs, GANs, and Diffusion pipelines using Hugging Face Diffusers.

Experiment Management, Tuning & Debugging

Course 3, 1 hour

What you'll learn

Apply LoRA and QLoRA fine-tuning to large language models using Hugging Face PEFT, comparing VRAM usage, throughput, and task performance.
Design and execute hyperparameter optimization sweeps on Azure ML using Bayesian sampling, early termination, and MLflow experiment tracking.
Diagnose training failure modes, including gradient explosion, overfitting, and normalization errors, using PyTorch Profiler and ablation studies.
Build high-throughput data pipelines using WebDataset, LMDB, and Azure ML Data Assets to eliminate I/O bottlenecks and maximize GPU utilization.

Distributed Training & Advanced Application

Course 4

What you'll learn

Build PyTorch DDP and FSDP pipelines for multi-node distributed training, profiling communication overhead, and scaling GPU throughput.
Configure DeepSpeed ZeRO optimization stages and apply Microsoft Olive and Azure Container for PyTorch to accelerate large model training.
Build computer vision and NLP pipelines for object detection, NER, classification, and QA using Florence-2, CLIP, and Hugging Face Transformers.
Design multimodal fusion architectures combining vision, text, and audio using Microsoft Phi-4 and OpenAI Whisper on Azure ML.

Model Optimization, Inference & End-to-End Engineering

Course 5

What you'll learn

Apply post-training quantization, pruning, and knowledge distillation to compress models and benchmark accuracy-latency trade-offs.
Configure ONNX Runtime with CUDA and TensorRT execution providers to accelerate inference across hardware targets.
Deploy containerized models to Azure ML online and batch endpoints using autoscaling and blue/green deployment patterns.
Architect and document a complete deep learning engineering lifecycle from distributed training through production deployment.

Launch Your Deep Learning Career

Course 6

What you'll learn

Optimize your resume to target deep learning engineer, MLOps, and AI infrastructure roles.
Structure and present complex engineering projects as portfolio-ready evidence of your skills.
Prepare for technical interviews with strategies aligned to advanced AI engineering positions.
Identify and pursue job opportunities in deep learning, MLOps, and AI infrastructure.

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Microsoft

424 Courses2,859,128 learners

Offered by

Microsoft

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Frequently asked questions

This program is designed for machine learning practitioners, data scientists, and software engineers who want to specialize in deep learning engineering. You should be comfortable with intermediate Python programming, have hands-on experience building ML models, and have basic familiarity with cloud environments before beginning.

Deep learning engineers design, train, optimize, and deploy neural network models that power AI applications—from image recognition and natural language processing to generative AI. Day-to-day work includes building training pipelines, fine-tuning model architectures, managing experiments, compressing models for production, and deploying inference services to cloud or edge environments. These skills apply across roles, including Machine Learning Engineer, MLOps Engineer, AI Infrastructure Engineer, and Applied Scientist.

You'll work with PyTorch, Azure Machine Learning SDK v2, MLflow, Hugging Face Transformers and Diffusers, DeepSpeed, FSDP, ONNX Runtime, TensorRT, Microsoft Foundry, and Azure Container Registry, covering the core toolchain used by professional deep learning engineers.

You'll complete five engineering projects — one per long course — producing deliverables such as training pipeline configurations, PEFT comparison guides, distributed training scaling reports, multimodal architecture documents, optimization benchmarks, and deployment runbooks. These projects are modeled on real enterprise engineering scenarios and are designed to be portfolio-ready artifacts you can share with employers or discuss in technical interviews.

An active Azure subscription is not required for the primary gradable track. All labs include a precomputed reference path using pre-baked logs, metrics, and execution outputs so you can complete the full curriculum without provisioning live GPU compute. Learners with active, corporate-funded Azure subscriptions and allocated GPU quota can optionally follow the live execution path provided throughout the program.