Some researchers will want to write their own C3 package and leverage more of the AI Suite. C3 allows researchers to define their own types and methods to integrate their data into the C3 AI Suite along side the Datalake. This allows researchers to use C3 data analytics methods such as timeseries metrics just as they would on other Datalake data. Researchers will also have the ability to share their data with other researchers in the DTI by sharing their package. Adding another researcher's package as a dependency to your package will also bring another researcher's data into your package as well.

Pros:

• Define custom types and integrate new data into the C3 Datalake.
• Share data and methods through C3 packages.

Cons:

• C3 credentials and tenant/tag are required.

Necessary Resources/Training:

• C3 Getting Started Guide
  • Not all sections of the Getting Started Guide are necessary.
    We list the necessary sections here:
    • Introduction to C3 AI Suite

    • Architecture

    • Data Integration
    • Methods
    • Time Series Data Section
    • Metrics Section
    • Data Science
    • C3 Developer Console
• C3 Provisioning Guide
  • Describes how to provision new code to a C3 environment
• DTI Types Exercise (In Development)
  
  • A simplified version of the official C3 training Example lightbulbExercise.

Optional Resources/Training:

• DTI Guide to utilizing C3 Documentation
• DTI C3 Tips and Tricks

Worked Examples:

• Phylogenetic Tree building example (C3 Package) (PLANNED)
• mnist Example (C3 Package) (PLANNED)
• House Coverage Example (C3 Package) (PLANNED)