Ranking and updating machine learning models based on data inputs at edge nodes

A system comprising:

• a memory having computer readable instructions;
• and
• one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors to perform operations comprising:
• receiving, by a processor, an input dataset for training a new machine learning model;
• performing, by the processor, for a plurality of trained machine learning models:
• retrieving a hash function and a sketch of a training dataset used to train the machine learning model;
• computing a sketch of the input dataset based on the hash function and the input dataset;
• and
• computing a distance between the sketch of the training dataset and the sketch of the input dataset;
• ranking the plurality of trained machine learning models from smallest computed distance to largest computed distance;
• selecting a seed machine learning model for the input dataset from the plurality of machine learning models, the selecting based at least in part on the ranking;
• and
• initiating a training process of the new machine learning model based at least in part on the selecting, the training process based at least in part on the selected seed machine learning model and the input dataset.

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations comprising:

• receiving, by the processor, an input dataset for training a new machine learning model;
• performing, by the processor, for a plurality of trained machine learning models:
• retrieving a hash function and a sketch of a training dataset used to train the machine learning model;
• computing a sketch of the input dataset based on the hash function and the input dataset;
• and
• computing a distance between the sketch of the training dataset and the sketch of the input dataset;
• ranking the plurality of trained machine learning models from smallest computed distance to largest computed distance;
• selecting a seed machine learning model for the input dataset from the plurality of machine learning models, the selecting based at least in part on the ranking;
• and
• initiating a training process of the new machine learning model based at least in part on the selecting, the training process based at least in part on the selected seed machine learning model and the input dataset.