1. A method of training and providing a machine learning model, the method comprising: obtaining, by one or more computers and as input, training data, the training data comprising: a plurality of training examples and, for each of the training examples, a respective label, wherein each of the training examples includes a plurality of machine learning features; training, by the one or more computers, the machine learning model on the training data, training the machine learning model comprising: storing a value of a first statistic associated with a first machine learning feature of the plurality of machine learning features as a low precision representation in a first memory location, obtaining, based on a first training example of the training examples, a first updated value for the first statistic during the training of the machine learning model, determining a loss of precision from an absolute loss that would result from storing the first updated value by updating the low precision representation in the first memory location, determining that the loss of precision exceeds a threshold value, in response, storing the first updated value for the first statistic by storing a high precision representation in a second memory location that has a higher storage capacity than the first memory location, generating an association between the first memory location and the second memory location, receiving a request for the value of the first statistic for use by the machine learning model, determining that the low precision representation in the first memory location is associated with the high precision representation in the second memory location, and providing, in response to the request, a combination of the low precision representation in the first memory location and the high precision representation in the second memory location as the value of the first statistic; and providing, by the one or more computers and as output, the trained machine learning model.