About Machine Learning for Decision Support Systems

Due to the ever-changing and highly competitive retail and business climate, decision-makers need all of the help they can get. Machine Learning for Decision Support Systems (MLDSS) is a new technology that aids decision-makers by using machine learning to automatically detect patterns and provide intelligent guidance. MLDSS enhances decision-makers’ capabilities by providing inputs such as data sets and algorithms that can improve their decision-making abilities. With MLDSS, decision-makers can optimize the value of their organization, and maximize profit while minimizing risk. As a result of MLDSS, decision-makers can understand everything that is happening in the organization and business environment and efficiently set and achieve optimized objectives.

Machine Learning is the fastest-growing field of Artificial Intelligence. It is quickly becoming one of the most important developments in the field. It offers significant benefits for organizations looking to speed up time in which they get to an application. Since it focuses on preventing problems before they happen, not only does Machine Learning help to improve results, but it also avoids costly mistakes. It can also be used to detect fraud, hidden patterns, or other anomalies.

This technology is growing so quickly because it offers significant benefits. It can be used to reduce costs, increase trust, and increase customer satisfaction. It can also enable organizations to anticipate events before they happen so they can be prepared. Machine Learning is the future of Artificial Intelligence.

Team and Performance

Quality Assurance together with Project Management and Business Analysis ensure that the technical implementation also works for the end clients. The team includes PM, BA, WA, a number of backend Java developers, a number of Angular/JS frontend developers and ML consultants.

The team is organized on Agile principles (SCRUM) and meets on a daily basis, having biweekly deliverables where the client’s product owner reviews the implementation and pivots through the following roadmap items.

Technology Stack and Architecture

The system is based on Java backend using Spring Boot. The backend is connected with the database that is hosted in PostgreSQL. The Machine Learning system is developed in Python using well known libraries and frameworks, including but not limited to Scikit Learn, NumPy, Pandas, matplotlib. Additional investigation has been done by the team for future deep learning extensions using TensorFlow or similar engines.