C3.ai Accelerates AI Application Development on AWS by 25X
To get the complete report, click on the following Download Report button. No information required.
The AWS cloud computing and AI microservices are powerful technologies to enable digital transformation. But developing enterprise AI applications on the AWS cloud requires significant assembly of bespoke services and can be complex and time-consuming.
Download this report to learn how using the C3 AI Suite dramatically simplifies and accelerates development of enterprise AI applications on AWS by a factor of 25x. Written by an independent system integrator, the report provides a detailed comparison of two approaches in developing AI applications on AWS: (1) using only AWS native services, and (2) using the C3 AI Suite on AWS.
Read the full report to see how the C3 AI Suite:
- Delivers a complete platform for enterprise AI application development
- Eliminates complex AWS infrastructure provisioning tasks
- Provides an abstraction layer through a model driven architecture removing the need to integrate AWS microservices
- Reduces coding effort by 50X or more
- Accelerates developer productivity by 25X or more
- Speeds time to deployment by 15X or more
About the Project
The independent system integrator is a Premier AWS consulting partner with AWS competencies in Big Data and Machine Learning, and have developed and deployed hundreds of applications on AWS for hundreds of Fortune 1000 customers.
At the onset of the project, the team agreed to eliminate the need for the low-level management of server and network resources. This worked well in practice and provided developers with the flexibility to manage multiple simultaneous workstreams. By breaking the application into numerous independent microservices/ components, the team was able to work in parallel integrating each service while avoiding code contention with the other developers.
The architecture for the AWS Application made heavy use of AWS managed services, including AWS Lambda for serverless processing, Amazon Kinesis for data streaming, Amazon S3 for storing raw data, Amazon API Gateway for RESTful services, and Amazon SageMaker for machine learning/artificial intelligence training and inference. For persistence, Amazon’s Relational Database Service (RDS), and Amazon DynamoDB, a NoSQL distributed key-value store database was used. This architecture stems from the team’s collective years of experience working with AWS services.