To develop an effective enterprise AI or IoT application, it is necessary to aggregate data from across thousands of enterprise information systems, suppliers, distributors, markets, products in customer use, and sensor networks, in order to provide a near-real-time view of the extended enterprise.
Today’s data velocities are dramatic, requiring the ability to ingest and aggregate data from hundreds of millions of endpoints at very high frequency, sometimes exceeding 1,000Hz cycles. The data need to be processed at the rate they arrive, in a highly secure and resilient system that addresses persistence, event processing, machine learning, and visualization. This requires massively horizontally scalable elastic distributed processing capability offered only by modern cloud platforms and supercomputer systems.
The resultant data persistence requirements are staggering. These data sets rapidly aggregate into hundreds of petabytes, even exabytes. Each data type needs to be stored in an appropriate database capable of handling these volumes at high frequency. Relational databases, key-value stores, graph databases, distributed file systems, and blobs are all necessary, requiring the data to be organized and linked across these divergent technologies.
C3 AI Suite’s model-driven architecture enables organizations to deploy applications on multiple public cloud platforms as well as on bare metal behind the firewall in a private cloud or data center.
A final requirement for the new AI technology stack – that the C3 AI Suite fully delivers – is polyglot cloud deployment capability: the ability to mix various services from multiple cloud providers and to easily swap and replace those services. The cloud vendors provide the market a great service by enabling instant access to virtually unlimited horizontally scalable computing capacity and effectively infinite storage capacity at exceptionally low cost. As the cloud vendors aggressively compete with one another on price, the cost of cloud computing and storage is approaching zero.
A second important service cloud vendors provide is rapid innovation of microservices. Microservices like TensorFlow from Google accelerate machine learning. Amazon Forecast facilitates deep learning for time-series data. Azure Stream Analytics integrates with Azure IoT Hub and Azure IoT Suite to enable powerful real-time analytics of IoT sensor data. It seems not a week goes by without another announcement of yet another useful microservice from Azure, AWS, Google, and IBM.
Polyglot Cloud Deployment
C3 AI Suite’s model-driven architecture provides polyglot capability, enabling application portability from one cloud vendor to another and the ability to run AI and IoT applications on multiple clouds simultaneously.
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