I think you and startupdiscus are using two different notions of "my own model". I think by "my own model" you mean that I can use my own data to train a pre-existing models that is built into ML.net. Typically, that's not quite what "my model" means. Say I come up with a completely new learning to rank algorithm, or I fancy a new one that I saw in one of the ML conferences. Now if I were to build that with ML.net that would be "my model" according to startupdiscuss and many others.
In startupdiscuss' sense, ML.net would allow building own models if it offers building-blocks, abstractions, and other facilities, for examples, vectors, matrices, tensors, linear algebraic operations, computation-graph, etc that allow building them.
In startupdiscuss' sense, ML.net would allow building own models if it offers building-blocks, abstractions, and other facilities, for examples, vectors, matrices, tensors, linear algebraic operations, computation-graph, etc that allow building them.