Overview

Coleman AI Platform represents a complete ensemble of tools for creating, managing, and deploying predictive and prescriptive models and associated use cases for the enterprise. Enterprises utilize artificial intelligence (AI) to leverage on multiple machine learning (ML) algorithms in conjunction with additional intelligence technologies and knowledge-engineered human decision logic with the aim of maximizing sales, reducing costs, boosting efficiency and generating insights.

It is at the Coleman AI Platform core to focus on creating value through predictive and prescriptive analytics with the goal to achieve the most efficient business decisions. In order to achieve this goal, Coleman AI Platform offers:

  • Machine learning based on the concepts of predictive analytics.
  • Optimization based on the concepts of prescriptive analytics.

These methods have similarities and differences.

The similarities are:

  • Runs on data and requires extensive computing resources, data availability, and data quality.
  • Involves deep mathematics to solve complex business problems.
  • Manages complexity and achieves better business outcomes.

The differences are:

  • Predictive analytics does predictions for future based out of big historical data by identifying the patterns and learning from past.
  • Prescriptive analytics helps in decision making to achieve goals by leveraging the latest data, mathematical model of the business and solver to generate the solutions empowering the best possible business decisions.

Machine learning techniques provide predictions based on data retrieved from your business applications. Coleman AI Platform uses programmed algorithms that analyze input data to produce a model that predicts and suggests outcomes for a specific business case. As new data is processed through the algorithm, models learn to optimize their operations and improve performance, gradually providing more accurate predictions. Machine learning can project outcomes and future business tendencies and behaviors, as well as perform classifications and recommendations. It learns from observations, identifies patterns in data, and explores different options and possibilities. These predictions can further advance in optimization and automation of business processes.

You can use the machine learning module to interactively explore data, transform it, develop, train, test, and compare machine learning models. Then you can choose and deploy the most efficient and most accurate model for your needs. You can evaluate model metrics, retrain and redeploy new versions of the model, while tracking its performance. Pre-built and pre-configured machine learning algorithms and data-handling modules are available.

Optimization on the other side, as a prescriptive analytic tool, helps you decide what decision to take or what you should do in order to reach your business goals. It is a field of applied mathematics which principles and methods are used to find the best possible solution to a complex real-life problem. The word optimization itself defines that you are making the best decision or ensuring the most effective use of a situation or resource. Optimization can be used to address large scale and critical business problems by either maximizing or minimizing the outcome as per the defined set of elements - available information, what you control, limitations/boundaries and goals to achieve. The model improves the solution over the number of iterations and generates the most optimal solution.

Coleman AI Platform has the ability to explore data, transform it, develop to ensure data availability and quality. You can define the mathematical model by configuring the elements, sets, indices, constants, decision variables, constraints, and objective function. You can then apply the solver to generate the most optimal solution. It also enables an option to develop multiple such models in the same space by applying different solvers available in the list. You can compare and evaluate the outcome of all the configurations, and then you can choose to deploy the most effective configuration/model for the processing as per business needs.