This Azure for Research hands-on lab was hosted by the University of Texas at Austin, and offered to faculty, researchers, and PhD students who want to learn how cloud computing can help deliver better, faster, and more reproducible research. This lab focused on Big Data Storage, Machine Learning and Advanced Analytics, Cognitive Intelligence and Azure Compute. The one-day training was delivered through a hands-on, scenario-based program.
- Attendees were able to access Microsoft Azure on their own laptop during the training and for evaluation purposes for one month after the course. The attendee’s laptop does not need to have the Windows operating system installed—Microsoft Azure is accessed via your Internet browser.
- This course is suitable for researchers using any language, framework, or platform. This includes Linux, Python, R, MATLAB, Java, Hadoop, STORM, SPARK, and Microsoft technologies such as C#, F#, Microsoft .NET, Microsoft Azure SQL Database, and various Microsoft Azure services.
- Some basic exposure to cloud computing is helpful, but no real expertise or usage experience is required. The focus of the class is to teach this to you.
- Several of the learning outcomes include:
- Learning how to create storage accounts and storage containers, upload and download blobs using the cross-platform Azure Storage Explorer, and securely share data hosted in blob storage using shared-access signatures
- Using the interactive Azure Machine Learning Studio to build, train, and score a machine-learning model, then putting the model to work by performing predictive analytics
- Building a web site for uploading photos and pass each uploaded photo to the Computer Vision API to generate captions and search metadata.
- Deploying a SLURM cluster of Linux servers and use a Python script to perform parallel processing on a collection of images
Lunch was provided.
This event was co-hosted by Microsoft Research, the University of Texas at Austin, and the South Big Data Hub.
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