C3 IoT Update: An Artificial Intelligence Platform for Enterprise IoT?


“Interest in artificial intelligence is surging.”

A major update to C3 IoT’s enterprise Internet of Things platform places a heavy emphasis on artificial intelligence along with edge analytics, application development, and seamless data science.

“Enterprise interest in AI is surging,” said a Forrester Research report. “We expect enterprise interest in, and use of, AI to increase as software vendors roll out AI platforms and build AI capabilities into applications. Enterprises that plan to invest in AI expect to improve customer experiences, improve products and services, and disrupt their industry with new business models.”

C3 IoT leverages Amazon Web Services for infrastructure and IoT device management; offers out-of-the-box applications while also allowing enterprises to build their own; and includes integrated AI and machine learning capabilities. The company’s platform is gaining heavy traction among large utilities — C3 IoT has signed some of the world’s largest — that use the platform for use cases such as predictive maintenance, customer experience, and billing. C3 IoT hopes to apply its use cases from the utility sector to other verticals such as manufacturing and aviation.

Version 7 of C3 IoT’s platform is designed to reduce IT expenditures while speeding the time to market for predictive analytics applications that perform AI and machine learning at scale, the company said. Updates in Version 7 include:

Operationalized data science

C3 Ex Machina, a visual analytics and machine learning development tool, is designed to simplify and speed big data exploration and predictive analytics modeling, without writing any code.

The platform  has “been created with the idea of empowering a few users to do a lot of work, while at the same time broadening the potential user base by requiring more business acumen than technical knowledge from platform users,” said Holger Mueller, vice president and principal analyst at Constellation Research.

Native support for Python and R, meanwhile, allows data scientists to leverage their programming language of choice and push their code back to production in the C3 IoT Platform without re-writing any code.

Enterprise AI / machine learning

The update includes additional deep learning technologies and more advanced analyses that increase the precision and accuracy of predictions. C3 IoT also added image processing for object and facial recognition at the edge; natural language processing (NLP) for analyzing text-based data such as hand-written notes and work logs; expanded support for libraries of machine learning algorithms; and a machine learning pipeline “that makes it faster and easier to develop and deploy machine learning models and publish them back to the platform,” the company said.

Application development

The C3 Type System, a high-level abstraction layer that enables disparate users to interact with the same metadata-driven architecture for defining and developing applications, now offers native HDFS integration to connect to customers’ data lakes as well as several enhancements that improve performance and optimize storage. (Related: Special report: Why companies use Hadoop.)

A new version of C3 IoT’s metadata-driven, web-based development toolset, C3 Tools, improves the user’s ability to define integration processes, create and extend data models and analytics, build machine learning classifiers, and rapidly develop user interfaces.

Also, a new Eclipse plug-in gives developers additional tools to develop, deploy, and debug applications on the C3 IoT Platform. And in addition to Apache Cassandra, C3 IoT now provides full support for AWS DynamoDB, a fast and flexible NoSQL database service for applications that need consistent, single-digit millisecond latency at any scale.

Edge integration with Amazon Web Services (AWS)

Tighter integration with AWS IoT and Lambda “lets connected devices easily and securely interact with the C3 IoT Platform,” the company said. Also, support for the C3 Type System at the edge enables customers to process and execute analytics on edge devices.

Intelligent enterprise PaaS

The company streamlined and improved platform administration tools, including the ability to manage environment configurations, manage auto-scaling configurations, and enable self-healing of common issues.


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Chris Raphael

About Chris Raphael

Chris Raphael (full bio) covers fast data technologies and business use cases for real-time analytics. Follow him on Twitter at raphaelc44.

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