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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.

If you want more background on the C3.ai platform, there is a one-hour C3.ai DTI webinar describing its
capabilities here: https://www.youtube.com/watch?v=cYoU1CTw8K8.

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
implementing new data processing functions in python and javascript.

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.

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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.

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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, metrics access to some time series data such as case data,
and allows you to pull local copies of those objects and metrics results into your local compute environment. 

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Full access to the Datalake offers access to all stored COVID-19 Datalake data while still allowing the researcher to use whatever
analysis framework they so choose with their own compute resources. This level offers the fastest startup time while still 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.

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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 – either independently or alongside the COVID-19
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.

Level 4: Advanced C3 Platform Usage (In Progress)

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.

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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. The process will be:

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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.

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If you feel aspects of this guide are incomplete or inaccurate, please send an email to help@c3dti.ai with the
issue or suggestion, and we will work to incorporate it to make the documentation better. We appreciate the new perspective more
More eyes can bring to a software project!

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