Building an AI application requires many teams — data science, platform, applications, products, design — to be involved in the process. Yet many current organizations are not equipped for intra-team and interdepartmental collaboration. On top of that, every time an enterprise wants to build a new AI application, it must start from scratch. This leaves companies struggling when they want to quickly put models into production, then scale them, and reuse previous work to accelerate development in the future. As a result, even though about half of organizations have adopted AI in one function, only 10–30% have begun scaling it across their enterprises through successful application development and deployments, according to a McKinsey survey.
Mehdi Maasoumy, Vice President, Data Science, C3 AI, recently led a webinar on how C3 AI solves these problems with the C3 AI Platform. Typically, he explains, there are three phases of AI application development where companies stall — first, going from prototype to a live, operationalized application; second, scaling a live application to the same use case, but for a different set of equipment or in a different environment; third, building a new AI application for another use case.
C3 AI addresses these efficiency issues with the C3 AI Platform, which enables developers and data scientists to design, develop, and operationalize large-scale AI applications on a single platform. One unique feature of the C3 AI Platform that helps make the development to deployment process seamless is the C3 Type System, an abstraction layer that allows developers to connect data sources, models, features, and other application deployments even if their source codes vary.
With the C3 AI Platform, developers can build upon previous applications, cutting down development time. He explained that when building a second use case, a developer can reuse 40% of code from the first and as much as 70% for a third. Therefore, the more models that are deployed, the faster and easier it is to deploy them.
This abstraction layer also allows organizations to future-proof their investment in AI applications. Using a common platform allows for easy connection (and disconnection) to any technology investments, such as any cloud service or on-premise data source, and gives enterprises the ability to easily swap these third-party services in the future.
The C3 AI Platform bridges the gap between teams, creating a more collaborative development process — offering a space where all developer personas from data integration engineers and application developers to data scientists are working against the same object models and code repository. In turn, enterprises can move from the prototype to a live application more efficiently than ever.
To see the C3 AI Platform in action, watch Amir Delgoshaie, Senior Manager, Data Science in our webinar demonstrate the benefits of using a model-driven architecture to re-use models, features, and components to expand an AI application across a fleet of wind turbines, Empowering Data Science Teams for AI Application Development.
