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Examine the high level overviews of each level below, then click the section titles to go to more in-depth
discussions related to that level, like the recommended training.

Level 1: Use Public COVID-19 Datalake Only

For many researchers, they will simply want to leverage the C3.ai COVID-19 Federated Data Image.

Pros:

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Cons:

  • All data used from the Datalake must be streamed to wherever you're processing data
  • Performance benefits from working with the Datalake using C3.ai will not be available.

Level 2: GUI based data analysis on C3.ai

C3.ai provides a wonderful GUI-based interface to the C3.ai system with their Integrated Development Studio. Such
an environment is likely to be attractive to many researchers. This level is the easiest way to integrate new
data onto the Datalake.

Pros:

  • GUI interface to manage C3.ai Types and data integration.
  • GUI interface to piece together ML pipelines.
  • Ability to load new data onto the Datalake

Cons:

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Level 3: Utilize C3.ai Suite and Jupyter notebook analysis

Some researchers will want to write their own C3.ai package and leverage more of the AI Suite through Jupyter notebooks.
C3.ai allows researchers to define their own types, methods, and use R and python to perform analysis on Datalake data.

Pros:

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Cons:

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Level 4: State-of-the-art ML workflows requiring special ML models and/or GPUs

Some researchers will want to bring state-of-the-art ML workflows to C3.ai. C3.ai can support such workflows, but
extra work may be needed.

Pros:

  • Researchers can bring state of the art workflows close to the COVID-19 Datalake

Cons:

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Accessing C3.ai

This section introduces the process to access C3.ai. Generally speaking, once you receive your grant,
the DTI team will reach out and discuss with you what your needs are.

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