You have been given a grant as part of the new C3.ai Digital Transformation Institute (DTI)!
To make the start of your DTI experience as fast as possible, we have assembled a set of resources to:
If you have questions not covered by this guide, please contact the DTI team at the email email@example.com
C3.ai is a data analytics engine designed to make the ingestion and analysis of heterogeneous data sources
as painless as possible. The C3.ai platform joins data from multiple sources into a single unified federated data image.
With the federated data image defined, C3.ai then provides an API to access that data, and in the case of time-series data,
perform numerous transformations and computations all producing normalized time-series data at regular intervals.
C3.ai also supports R and Python Jupyter notebook analysis of the federated data image. These notebooks provide a
great way for researchers to analyze data close to where the data is stored. While C3.ai supports many data science
capabilities familiar to the researcher, some expected functionality may be missing. For these cases, C3.ai supports
Like any other API porting your own workflows will take some care and time to learn properly. Please leverage this guide
to make understanding the C3.ai platform and porting your workflow as quick and easy as possible.
Use this guide to determine what training you need to utilize C3.ai resources effectively. We have identified four
categories of usage of the C3.ai platform. We include basic examples of workflows which might fall into that level,
pros and cons of operating on that level, and a list of training resources we recommend resources researchers
completing on the DTI training environment before starting their C3.ai allocations. This will ensure researchers will
be able to use their allocation as efficiently as possible.
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.
For many researchers, accessing the public API for the COVID-19 Federated Data Image will be enough for their research goals.
The public API provides fetch access to many datalake objects, and metrics access to some time series data such as case
Full access to the Datalake offers all stored COVID-19 Datalake data while allowing the researcher to use whatever analysis
framework they so choose with their own compute resources. This level offers the fastest startup time while ensuring
access to all data. Once you learn how to query data in C3, that data can be streamed to your compute resources where
you can use your language and tools of choice.
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.
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.
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.
C3.ai is quite different from traditional HPC resources. We have written an introduction to C3.ai from the
perspective of a scientific researcher. We go over several important C3.ai concepts and relate them to
what scientists are more familiar with.
This section introduces How researchers will be expected to manage their allocation while on the C3.ai platform.
This section will be expanded once the DTI team understands how this procedure will look to the researcher.
Here you can find information about the special compute resources available to C3.ai DTI researchers.
See the above link for a comprehensive list and categorization of the available training
materials. This includes C3.ai Documentation, DTI introductions, and DTI created examples and exercises.
No problem! You're not alone! Please send an email to firstname.lastname@example.org with a description of your issue
and one of our team will work with you to resolve your issue.
If you feel aspects of this guide are incomplete or inaccurate, please send an email to email@example.com with the
issue or suggestion, and we will work to incorporate it to make the documentation better. We appreciate the new perspective
More eyes can bring to a software project!
Jay Roloff - Executive Director
Matthew Krafczyk - Data Analyst
Yifang Zhang - Data Analyst
Larry Rohrbach - Executive Director