Machine Learning comprises a variety of techniques allowing applications to automate business decisions and continuously improve performance, without requiring explicit instructions. Historical data is used to train a model for making predictions. All business applications can make use of Machine Learning in a wide variety of use cases. Our approach benefits our customers by increasing their efficiency, bringing robustness to features and functions, and doing all of this while reducing costs.

Chainbridge Solutions works with customers to identify scenarios and provide proof of concepts that demonstrate the benefits of machine learning in their organizations and supporting mission systems. For example, we predict background investigation adjudication results based on data collected during the investigation. These prediction results help our customers rapidly pinpoint areas that need to be reviewed further leading to a more efficient and secure workforce. In addition, we also provide a confidence level that provides insight into how much additional work will be needed to complete the case. Efficiencies are also realized when Machine Learning demonstrates automatic task assignments based on employee availability and task details. This improvement reduces the need to create queues for distributing work, saving our customers time and money.

Our machine pipeline consists of 3 steps:

  • Analyze – We analyze customer data model and use cases to determine the data to be incorporated into the machine learning solution. Using a scheduled job to continuously collect information from the system, we transform it into a suitable training set. Data parsing, encoding, and normalization is also performed during this analysis step. As time progresses, the amount of data in the system available for training increases and as a result, the generated models will become more accurate.
  • Train – Using the training data set created during the analysis in step 1, we build and publish a customer-specific predictive model. Training the data includes the selection of algorithms and customization of features to generate a set of models. Based on the test results, the model with the best performance is saved for use in the application.
  • Enhance – Using the generated and selected predictive model, our team enhances the application. These enhancements typically include updating the user interface to augment the data available to the user and simplifying the application by replacing complicated business logic with the model output.

In our approach, providing flexibility as a consideration at every point in the pipeline is a key benefit in providing tangible results for our customers. Several existing components and libraries are leveraged, with all steps being configurable driven by customer needs. This allows us to make changes to the feature set and data transformation process as the application evolves. In addition, the pipeline gives us a strategy for continuous learning so new models can be generated periodically, remaining current by using the latest data set. At any point, new algorithms and techniques can be used to construct additional models that may outperform the baseline. Lastly, our approach keeps all sensitive user information within the existing system, ensuring data is secure and free from unauthorized access and alteration.