During one of the in-person API reviews we agreed that it would be a good idea to have a single object MLContext that would serve as a 'factory of everything' (similar to the HTTP context / DB context in the .NET world).

• MLContext will explicitly implement IHostEnvironment, so you can create all the existing estimators by giving the context as the first argument.
• MLContext will have properties BinaryClassification, Regression, Clustering etc. for canonical ML tasks (the ones that are currently classes in themselves), complete with Evaluate and all corresponding trainers.
• It will have extension methods for non-canonical tasks like recommendation or anomaly detection etc.
• It will have properties Transformation, Filtering, Loading to instantiate all known transform estimators, filters and data readers (again via extension methods).
• It will have a pair of methods SaveModel and LoadModel that handle model serialization.

/cc @KrzysztofCwalina @TomFinley @eerhardt @markusweimer @asthana86