Data management, reduction and sampling schemes for storage device failure

A data system comprising:

• a database;
• a processor coupled to the database;
• and
• a memory coupled to the processor, wherein the memory stores instructions that, when executed by the processor, cause the processor to:
• segment a dataset from the database into one or more datasets based on time period windows;
• assign one or more weighted values to the one or more datasets according to the time period windows of the one or more datasets;
• generate a training dataset from the one or more datasets according to the one or more weighted values;
• and
• train a machine learning model using the training dataset.

A method for training a machine learning model, the method comprising:

• identifying, by a processor, anomaly data in a dataset from a database;
• generating, by the processor, anomaly data;
• adding, by the processor, the generated anomaly data to the dataset;
• identifying, by the processor, a training dataset from the dataset;
• retrieving, by the processor, the training dataset from dataset;
• and
• training, by the processor, the machine learning model using the training dataset.