Experiment Management, Tuning & Debugging

This course is part of Microsoft Deep Learning Engineering with Azure Professional Certificate

Instructor: Microsoft

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.

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Assessments

17 assignments¹

AI Graded see disclaimer

Taught in English

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This course is part of the Microsoft Deep Learning Engineering with Azure Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

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There are 9 modules in this course

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.

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.

What's included

1 video2 readings1 assignment

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.

What's included

3 readings3 assignments

3 readingsTotal 13 minutes

Minimizing Infrastructure Footprints with QLoRA3 minutes
Quantization Matrix Layers & Target Module Topologies5 minutes
PEFT Configuration and Integration Rules5 minutes

3 assignmentsTotal 70 minutes

Graded Quiz: Parameter-Efficient Fine-Tuning30 minutes
Hands-on Activity: Fine-Tuning a 7B Parameter Language Model with QLoRA30 minutes
Knowledge Check: Implementing LoRA and QLoRA with Hugging Face PEFT10 minutes

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.

What's included

1 video3 readings1 assignment

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.

What's included

1 video2 readings3 assignments

1 videoTotal 4 minutes

MLflow Integration Patterns and Artifact Lineage4 minutes

2 readingsTotal 9 minutes

The Necessity of Centralized Telemetry Registries3 minutes
Ingesting and Comparing Large-Scale Reference Runs6 minutes

3 assignmentsTotal 65 minutes

Graded Quiz: Hyperparameter Optimization & Experiment Management on Azure ML30 minutes
Hands-on Activity: Hyperparameter Search Analysis and Selection Report25 minutes
Knowledge Check: Logging Syntax & Studio Views10 minutes

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.

What's included

1 video2 readings1 assignment

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.

What's included

3 readings3 assignments

3 readingsTotal 15 minutes

Generalization Boundaries in High-Capacity Architectures4 minutes
Deep Dive: RMSNorm, LayerNorm, and Data-Mixing Strategies6 minutes
Implementing Normalization and Data Augmentation in PyTorch5 minutes

3 assignmentsTotal 70 minutes

Graded Quiz: Regularization & Robustness30 minutes
Hands-on Activity: Implementing a Regularization Stack and Ablation Blueprint30 minutes
Knowledge Check: Advanced Regularization, Normalization & Ablation Frameworks10 minutes

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.

What's included

1 video3 readings1 assignment

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.

What's included

1 video2 readings3 assignments

1 videoTotal 5 minutes

Ingestion Modes & PyTorch Worker Coordination5 minutes

2 readingsTotal 9 minutes

Scalability Limits of Local Disk Storage4 minutes
Writing High-Throughput Streaming Loops in PyTorch5 minutes

3 assignmentsTotal 66 minutes

Graded Quiz: Scalable Data Pipelines30 minutes
Hands-on Activity: Engineering a High-Throughput Data Pipeline Blueprint26 minutes
Knowledge Check: Designing High-Throughput Streaming DataLoaders10 minutes

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.