DBMiner- Adaptive Machine Learning System

The industry’s standard modeling tools do a great job of solving moderately complex problems, but they still require a significant amount of manual effort when data involves varied complexities and relationships that are unstable and hard to locate. DBMiner was developed to supplement the dominant modeling tools, empowering our clients to locate latent business opportunities in the data.

The software was designed and optimized to provide a scalable, flexible, and embeddable solution to predictive modeling problems. Priority has been given to ensure that all components are embeddable in a wide variety of production systems. The system is written in Java and provides SOAP and REST interfaces for non-java architectures.

Main Ideas and Principles
  • Complex feature creation: transforms raw data into descriptive numerical examples readable by machine learning systems, forcing the complexity of the model into the confines of the problem space.
  • Sequentially trainable machine learning models capable of handling extremely large data sets and data streams quickly, large-scale training, high-throughput testing.
    Regularization: many different regularization techniques are available, and can be mixed and matched, controlling the complexity of the model, and preventing over fitting.
  • Model selection: hyper-parameter tuning, finding the best model from the many candidate models.
  • Evaluation: a rich set of evaluation metrics is generated, enabling the assessment of the performance of predictive systems.
  • Configurability: machine learning systems can be designed by specifying the layout of the above components in a YAML file. This allows non-programmers to build production quality systems.
  • Feature Creation
  • Predictive Modeling
  • Regularization
  • Model Selections
  • Evaluation
  • Configurability