Your early experiments can use small data sets to create a working model, or perhaps use islands of data to prove a particular use case. But, as you scale AI, you’ll need to be able to access and process real business data for each use case, which can be problematic if your training data is stored in the public cloud. Without a dedicated server, you run the risk of slower development iterations and insufficient performance, which can hinder the success of your AI programs.
Scaling AI: Solving the Hidden Challenges
