Getting started

Last updated: October 10, 2023

You’re ready to move to the cloud, so what’s next? We offer:

  • Guidance on the best cloud computing solution or other innovative computing tools offered by UW-IT, in partnership with the UW eScience Institute
  • Help in learning about and using cutting-edge tools and practices in data science
  • Consulting and support: Contact us at UW-IT Research Computing

Recommended first steps

  • Decide if you want to apply for research computing credits. These credits count towards cloud costs in place of real dollars. See the application for Amazon Web Services (AWS), Google Cloud, and Microsoft, both  strong supporters of research computing. These short proposals are evaluated quarterly (AWS, Microsoft) or continuously (Google), and can result in substantial computing credit awards as high as $20,000. This is typically more than sufficient for a year of cloud-based data science support.
  • Match your research team up with the cloud process: Your research team has unique skills and methods. Matching them to the cloud means understanding how much time and money will be required to accomplish your goals, and deciding who will take responsibility for the pieces of that transitional process.
  • Look for training courses online and those offered at the UW, or talk to us about scheduling courses to help you and your research team navigate the cloud.
  • Plan to spend time learning how to manage your account: Allow time to learn how to manage users and resource groups, how to estimate costs, how to turn off resources you are not using, how to set usage alarms, how to avoid indirect costs on your grant budget, and how to make your cloud experience a success.
  • Contact cloud vendors: Office hours at the Data Science Studio in the Physics-Astronomy Building (PAB), 6th floor, are a great way to get acquainted with vendors. Vendors will often be happy to stop by your offices for in-depth conversations. See eScience office hours.
  • Include funding for cloud support in your research proposals: Funding agencies are developing guidelines for researchers wishing to include funding for cloud resources in their proposals.

Once you have laid your initial groundwork, be sure to take these final steps:

  • Watch for new cloud service offerings that can further enhance your research efforts: Cloud vendors are rapidly innovating new technologies, tools and services. If you can devote time and thought to exploring these new features—for example Machine Learning tools implemented as a service—you may well discover the cloud can enhance your research in many ways.
  • Join the UW data science community: We see cloud computing as just one of many important tools in the research computing toolbox. A fuller picture is represented by the collaborative efforts of UW-IT Research Computing and the UW eScience Institute. We are building a supportive community that strives to help support your research, and we encourage you to join us. Get started by visiting the Data Science Studio on the 6th floor of the Physics-Astronomy Building (PAB) or the eScience Institute. You’ll find consulting office hours, incubator sessions, workshops, seminars, hackathons and helpful scientists with outstanding data science skills, plus plenty of coffee.
  • Explore our technical documentation website.

More getting started information on cloud computing

Here is some additional information you will want to know:

  • UW has umbrella cloud agreements with vendors that can benefit you
    • A Business Associate Agreement (BAA) includes HIPAA-aligned technologies
    • No indirect costs on our grant budget (UW is the only university that does this)
    • The UW is cloud-agnostic, supporting Amazon Web Services, Microsoft Azure, Google and any other providers
    • The data egress waiver means you are not charged (up to 15 percent of the global monthly cost) for pulling data from the cloud; and pushing data up to the cloud is always free
  • Discover online tutorials and answers to technical questions
    • YouTube tutorial, GitHub repositories and knowledge bases abound online
    • Our technical documentation website is rapidly expanding based on UW case studies and in response to researcher requests
  • Find and follow best practices
    • Learn about data security and Virtual Private Clouds
    • Learn how to benchmark, test and speed up your data pipelines
    • Learn to estimate costs of cloud usage very accurately; and learn cost-saving mechanisms such as the AWS Spot Market
    • Learn about high-level services from databases to alarms to cluster management
    • Learn best practices, for example:
      • Test locally before you deploy to the cloud
      • Don’t accidentally publish access keys to repositories like GitHub
      • Learn how to use built-in redundancies to safeguard your data
  • Build modular
    • Data systems can be developed and tested in a modular fashion with clear boundaries (separation of roles)
    • Do not plan to build everything at once; build according to a logical order
    • Work backwards from ambitious goals around data-driven collaboration
      • Before you can integrate with other researchers, other systems: Build your analytics
      • Before you build analytics: Build data query methods
      • Before building data query methods: Enable data and code sharing
      • But start by setting up your storage
      • This sequence—storage to sharing to query to analytics to integration — helps you build your base dependencies first and avoid backtracking