Welcome to the C3.ai Digital Transformation Institute!
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:
- Introduce researchers of all stripes to the C3.ai system
- Help researchers determine what level of training they will need to leverage C3.ai's resources
- Point researchers directly to relevant documentation they will need
- Provide worked examples of different research workflows and how they may be ported into
the C3.ai environment, or may use C3.ai's resources
Introduction to the C3.ai system
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
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.
Services available from C3.ai
- Covid-19 Datalake: This unified federated Datalake includes data from numerous sources.
- C3.ai computing platform
- C3.ai Integrated Development Studio
- C3.ai Jupyter notebooks
- C3.ai Marketplace
- C3.ai UI system for creating dashboards
How does C3.ai differ from traditional HPC systems?
- Traditional HPC systems are similar to Hardware as a Service (HaaS), while C3.ai is more like a Platform as a Service (PaaS).
Users are encouraged to work within the platform's API to achieve the best performance out of C3.ai.
- C3.ai offers a state-of-the-art data integration system as the basis for all Data Science operations.
This is in contrast to HPC systems where all components of data management and the analysis pipeline must be installed and
What types of software can be run on C3.ai?
- Nearly any Python module may be installed and used through pip or conda.
- Nearly any R package may be installed and used within the R juptyer environment.
What types of software cannot be run on C3.ai?
- General binary executables are not supported by C3.ai out of the box.
- MPI-based python software
- Packages which must be built from scratch on the platform, or require specific hardware drivers
- Python modules which require special built binaries may not run as well.
How do I get started?
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 data,
and allows you to pull local copies of those objects and metrics results into your local compute environment.
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.
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.
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. The process will be:
- Determine which researchers will require access to a C3.ai environment
- Each researcher will be given a C3.ai developer portal login.
- Each researcher will be given a tag on the C3.ai DTI training cluster.
- Once training is complete, discuss with the DTI team what your needs
for a C3.ai cluster will be.
- The C3.ai DTI will work with C3.ai to stand up a new tag for your research.
- Access to that tag will be granted to your researchers
- Research can then proceed until your allocation is exhausted!
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.
C3.ai Allocation Management
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.
Help! This guide doesn't solve my problem!
If you feel aspects of this guide are incomplete or inaccurate, please send an email to firstname.lastname@example.org 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!
Your DTI Team
Jay Roloff - Executive Director
Matthew Krafczyk - Data Analyst
Yifang Zhang - Data Analyst
Larry Rohrbach - Executive Director
Eric Fraser - Chief Technology Officer
Greg Merritt - DevOps Lead
Matt Podolsky - Managing Director of Research Technology