Can you explain the differences between AutoML and ML-in-a-box?

AutoML is a pretty well-defined term. The way it works is, if I give you a set of annotated data—inputs, and outputs, you give me the best possible model for that data. An AutoML system is a system that searches the space of all possible ML models, or AI models, and returns the best fit for your data. Searching, meaning also training. But you do not just train one model; you train a big space of models. An AutoML engine is a part of any machine learning stack. 

When I say end-to-end ML or ML-in-a-box, I mean the type of company that Nyckel is building where you, as a customer, don't actually touch the ML models. It means that the APIs that you experience are at the data level. You're just giving the inputs and the outputs, and don't concern yourself with triggering training—you're not selecting models or tweaking any parameters. You're only concerned about your own data and what you want the model to do.

Most ML systems today rely on so called “transfer learning” where a AI model is pre-trained on a large general body of data and then tuned to work for your specific problem.Basically, the networks are so "smart" that with just a few little inputs, they learn to do what you want.