C3.ai Accelerates AI Application Development on Azure by 25X

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Microsoft Azure cloud computing and AI microservices are powerful technologies to enable digital transformation. But developing enterprise AI applications on the Azure 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 Azure 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 Azure: (1) using only Azure native services, and (2) using the C3 AI Suite on Azure.

Read the full report to see how the C3 AI Suite:

  • Delivers a complete platform for enterprise AI application development
  • Eliminates complex Azure infrastructure provisioning tasks
  • Provides an abstraction layer through a model driven architecture removing the need to integrate Azure microservices
  • Accelerates developer productivity by 25X or more
  • Speeds time to deployment by 8X or more
About the Project

The independent system integrator used for this project is an experienced Azure consulting partner with Azure competencies in Data Platform, Cloud Platform, Application Development, and Data Analytics, and have developed and deployed hundreds of applications on Azure for hundreds of Fortune 1000 customers.

At the onset of the project, their 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 Azure Application made heavy use of Azure managed services, including Azure Functions for serverless processing, Azure Stream Analytics for data streaming, Azure Data Lake Storage for storing raw data, Azure API Gateway for RESTful services, and Azure ML Studio for machine learning/artificial intelligence training and inference. For persistence, Azure Synapse, and Azure Cosmos DB, a NoSQL distributed key-value store database were used. This architecture stems from the team’s collective years of experience working with Azure services.

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