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Level 2: GUI based data analysis on C3
C3 provides a wonderful GUI based interfaced to the C3 system with their Integrated Development Studio. Such
an environment is likely to be attractive to many researchers.
Pros:
- GUI interface to manage C3 Types and data integration.
- GUI interface to piece together ML pipelines.
Cons:
- Some types of workflows may not be easily defined within the GUI framework
Level 3: Utilize C3 AI Suite and Jupyter notebook analysis
Some researchers will want to write their own C3 package and leverage more of the AI Suite through Jupyter notebooks.
C3 allows researchers to define their own types, methods, and use R and python to perform analysis on Datalake data.
Pros:
- Researchers can use a jupyter notebook to interface directly with their Data Model.
- Researchers can often use exactly the same workflow they were using before.
Cons:
- We recommend users take a full set of training to completely familiarize themselves
with the C3 system before embarking on their analysis.
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. C3 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:
- The DTI team will evaluate on a case-by-case basis whether a workflow is appropriate for C3.
- Some workflows may require major effort to fit within the C3 framework.
Accessing C3
This section introduces the process to access C3. 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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This section will be expanded once the DTI team understands how this procedure will look to the researcher.
Comprehensive List of Training Examples and Exercises
See the above link for a comprehensive list and categorization of training examples.
Comprehensive List of Available Training and Resources
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