Dell products l.p. (20240112070). DRIFT MODE ACQUISITION USING SUCCESSIVE MODEL TRAINING simplified abstract

DRIFT MODE ACQUISITION USING SUCCESSIVE MODEL TRAINING

Organization Name

dell products l.p.

Inventor(s)

[[:Category:Herberth Birck Fr�hlich of Florianópolis (BR)|Herberth Birck Fr�hlich of Florianópolis (BR)]][[Category:Herberth Birck Fr�hlich of Florianópolis (BR)]]

Vinicius Michel Gottin of Rio de Janeiro (BR)

DRIFT MODE ACQUISITION USING SUCCESSIVE MODEL TRAINING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240112070 titled 'DRIFT MODE ACQUISITION USING SUCCESSIVE MODEL TRAINING

Simplified Explanation

The abstract of the patent application describes a method for evaluating the drift mode of a machine learning model by defining a time window, training a reduced reference model with a subset of the training dataset, iterating through training new versions of the reference model, collecting data, comparing inference values, and determining the drift mode based on the differences.

• Explanation of the patent:

 * Define a time window for evaluating drift mode
 * Train a reduced reference model with a subset of the training dataset
 * Iteratively train new versions of the reference model
 * Collect data and compare inference values
 * Determine drift mode based on differences

1. 1. 1. Potential Applications

This technology can be applied in various industries where machine learning models are used, such as finance, healthcare, and e-commerce, to monitor and detect drift in model performance over time.

1. 1. 1. Problems Solved

This technology addresses the challenge of monitoring and detecting drift in machine learning models, which is crucial for ensuring the reliability and accuracy of predictions in real-world applications.

1. 1. 1. Benefits

- Improved model performance monitoring - Early detection of drift in machine learning models - Enhanced model reliability and accuracy

1. 1. 1. Potential Commercial Applications
         1. Monitoring Drift in Machine Learning Models for Enhanced Performance

This technology can be utilized by companies that rely on machine learning models for decision-making to ensure the models are performing optimally and accurately.

1. 1. 1. Possible Prior Art

There may be prior art related to monitoring and evaluating drift in machine learning models using different techniques or methodologies. Research in the field of model monitoring and evaluation may provide insights into similar approaches.

1. 1. 1. 1. Unanswered Questions
         2. How does this method compare to existing techniques for evaluating drift in machine learning models?

This article does not provide a direct comparison with existing techniques for evaluating drift in machine learning models. Further research or experimentation may be needed to determine the advantages and limitations of this method compared to others.

1. 1. 1. 1. What are the potential limitations or challenges of implementing this method in real-world applications?

The article does not discuss potential limitations or challenges of implementing this method in real-world applications. Factors such as computational resources, data availability, and scalability may need to be considered when applying this method in practical settings.

Original Abstract Submitted

one example method includes defining a time window during which a drift mode of a machine learning model will be evaluated, and the time window begins at t, training, beginning at the time t, a reduced reference model rwith a data sample dthat is a subset of a training dataset that was used to train the machine learning model; for ‘n’ iterations: at a time t, when new data samples dare available, train a new version rof the reduced reference model r; after ris trained, collect data v; and compare respective inference values generated by the reduced reference model rand r, using the data samples dand v, and store an absolute difference between the inference values; defining a drift mode curve using the absolute differences; and based on the drift mode curve, determining a drift mode of the machine learning model.