Actually, Reform is very simple and consists of several smaller objects.
Each object has a very specific scope one does exactly one thing, where the actual form object orchestrates between those. Twins are light-weight decorator objects from the Disposable gem.
Is this order valid to be filled, is this customer valid to check in to the hotel.
So rather than have methods like is Valid have methods like is Valid For Check In.
This blog post gives an early overview, code examples, and a few details of MLlib’s persistence API.
Key features of ML persistence include: Thanks to all of the community contributors who helped make this big leap forward in MLlib!
We can then import data, check if it looks correct, and easily serialize the results into any format we need.
In Apache Spark 2.0, the Data Frame-based API for MLlib is taking the front seat for ML on Spark.All of these use cases are easier with model persistence, the ability to save and load models.With the upcoming release of Apache Spark 2.0, Spark’s Machine Learning library MLlib will include near-complete support for ML persistence in the Data Frame-based API.Thinking about the context for validation may help prevent that. Forms that have 90% of the data but won't save anything are unfair to users, who often make up the other 10% just to not lose data, so all the validation has done is force the system to lose track of which 10% was made up.Similar issues can happen on the back end - say a data import.