Azure for Researchers

Unlock the power of Microsoft Azure to accelerate your research. This instructor-led course is designed for academic and institutional researchers who need cloud-scale resources, collaboration tools, and advanced data processing capabilities. Learn how to set up Azure environments for research, manage large datasets, utilize high-performance computing (HPC), and deploy AI and machine learning models. Whether you're working on scientific simulations, biomedical analysis, or humanities research, this course equips you with the skills to innovate faster and more efficiently using Azure.

Audience Profile

This course is intended for:

• Academic researchers and PhD candidates

• Institutional IT and research support staff

• Scientists and engineers in R&D roles

• Data analysts and data scientists working on research projects

Basic familiarity with research workflows and data handling is expected. No prior Azure experience is required.

Course Outline

Module 1: Introduction to Azure for Research

• Overview of Microsoft Azure and Research Support Programs (e.g., Azure Research Credits)

• Azure architecture and global infrastructure

• Azure services most relevant to researchers (e.g., VMs, Blob Storage, AI, HPC)

• Tour of the Azure Portal and CLI

Module 2: Storage and Data Management for Research

• Choosing the right storage: Blob, File, Disk, and Data Lake

• Ingesting, storing, and securing large datasets

• Organizing research data using containers and lifecycle policies

• Sharing data securely with collaborators

Lab: Upload and structure a research dataset in Azure Blob Storage

Module 3: Scalable Compute for Research Workloads

• Azure Virtual Machines for research computing

• Using Azure Batch and HPC clusters

• Cost management and automation tips

• Preconfigured research VM images and tools (e.g., genomics, MATLAB, RStudio)

Lab: Launch and configure a Linux VM for a research task

Module 4: AI and Machine Learning for Research

• Overview of Azure AI and ML tools for researchers

• Azure Machine Learning Studio and Notebooks

• Using pre-trained models and custom training

• Responsible AI principles in research

Lab: Train a model using Azure Machine Learning Studio with a research dataset

Module 5: Collaboration and Reproducibility

• Azure DevOps and GitHub for research code and CI/CD

• Using Azure Notebooks and ML pipelines for reproducibility

• Managing access with Azure Active Directory

• Integrating with Microsoft 365 for team collaboration

Lab: Set up a research project repo with CI/CD pipeline and data tracking

Module 6: Research Security, Compliance, and Cost Optimization

• Azure compliance for research data (e.g., HIPAA, GDPR)

• Securing sensitive and identifiable data

• Monitoring, budgets, and cost analysis tools

• Leveraging free and discounted services for researchers

Lab: Configure cost alerts, resource locks, and security policies

Capstone Project: Build a Reproducible, Scalable Research Workflow in Azure

Apply everything you've learned to deploy a simplified end-to-end research pipeline using Azure services. Choose from domains such as:

• Biomedical data processing

• Climate simulation

• Social science data analysis

• AI/ML-based classification