Document ID: CGQT154 | Last Updated: Oct. 29, 2018
Building Artificial Intelligence (AI) and machine learning (ML) applications is unlike building a traditional application development project. Most of the “functionality” of the systems are wrapped up in the data. Indeed, AI systems are very much data-centric rather than programming centric. Putting the final trained models into production is called “operationalizing” in the lingo of AI. As such, while traditional application development approaches work well for AI algorithm development and deployment, they don’t work as well for the data-centric needs of AI. Companies face a number of challenges in dealing with AI and ML implementations. The AI Company provides a platform that enables and speeds up AI app development by building a service and data abstraction in a cloud-based environment that abstracts much of the low-level data, visualization, and machine learning capabilities allowing their customers to focus on the applications that consume AI results rather than spend time building and managing the AI environment.