Overview

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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.

Syllabus

Parameter-Efficient Fine-Tuning Foundations & LoRA Mechanics
- Establish the foundation of parameter-efficient adaptation. This module explores why full fine-tuning scales poorly on enterprise hardware budgets and mathematically deconstructs how Low-Rank Adaptation (LoRA) compresses weight update tensors into low-rank matrices.
Implementing LoRA and QLoRA with Hugging Face PEFT
- Move to practical programming implementation. This module configures, instantiates, and executes quantized parameter-efficient configurations using Hugging Face PEFT, injecting low-rank adapters into attention targets while maintaining strict numerical stability parameters.
Managing Automated Sweep Jobs & Early Stopping on Azure ML
- Automate multi-trial hyperparameter exploration. You will structure cloud infrastructure blueprints to run parallel optimization sweeps using Azure ML SDK v2, defining parameter search spaces and applying early termination rules to drop stagnant training runs.
Experiment Tracking & Analysis with MLflow and Azure ML Studio
- Establish deep observational tracking over your training workloads. You will integrate MLflow tracking hooks into PyTorch scripts, capture essential training artifacts, and evaluate large-scale multi-trial tables inside Azure ML Studio to identify the optimal model configuration.
Tracking, Diagnosing & Resolving Gradient Anomalies
- Triage execution anomalies at their source. You will learn to use the PyTorch Profiler and TensorBoard to isolate gradient failures, calculate tensor norm thresholds, and implement gradient clipping to stabilize training steps.
Advanced Regularization, Normalization & Ablation Frameworks
- Stabilize model generalization limits. You will programmatically configure modern normalization structures (LayerNorm and RMSNorm), apply advanced data-augmentation frameworks via torchvision.transforms.v2, and implement structured ablation workflows to measure your code's robustness against severe overfitting.
Profiling I/O Bottlenecks & Implementing Cloud Storage Layouts
- Bridge the gap between storage layers and computing engines. Learners profile file ingestion patterns to locate hardware starvation issues, configure Azure ML data asset connections, and package individual assets into compressed sharded formats.
Designing High-Throughput Streaming DataLoaders
- Unleash the full performance of cloud streaming. Learners configure PyTorch DataLoader parameters for multi-threaded execution, balance input/output modes against local storage boundaries, and verify hardware utilization metrics.
Project Module: Pipeline Optimization & Tuning
- Synthesize your model-tuning and experiment-management skills to engineer an automated, memory-efficient fine-tuning pipeline. You will write a Python script that loads a large foundation model in 4-bit precision, configures Low-Rank Adaptation (LoRA) target modules, and instruments a custom training loop with MLflow metric tracking. You will then write the Azure ML SDK v2 configuration code to orchestrate a distributed hyperparameter sweep over your pipeline, utilizing a Bandit early stopping policy to optimize compute cluster resource allocations.