Automating Intelligence

Today’s world is witnessing some outstanding AI enabled applications that yields huge opportunities for industries to resolve their real time problems and generate value for their businesses. Amper’s collaborative ecosystem with AI uses training and validation data to help a model gain insight to enable it to predict outcomes from similar data patterns. The model uses the learning acquired to forecast future behaviour, patterns and trends accurately without being explicitly programmed for this task.

Solution

  • Amper provides a user friendly drag and drop environment to build a flow that can make use of the analytical capabilities of ML model and enables users to connect to other Data Science applications like GE Predix, IBM Watson, to leverage the accuracy and predictive intelligence of their ML models.
  • Amper also facilitates improving the accuracy of the ML models through its continuous learning capability that allows users to give feedback on model predictions, which can be re-introduced as training data to retrain the model and increase model accuracy.
  • The seamless connectivity between Amper’s process models, data models, event models and ML models presents Amper as a powerful platform that allows the user to take advantage of ML Models to build AI enabled complex business applications.
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Uniquness

Uniqueness

  • Amper’s high scalable algorithms ensure that the system is efficient even if the complexity of the input data increases.

  • The ML model can be integrated with process, event or data models effortlessly. The Machine Learning model can be integrated with the Data Flow model to enhance data preparation capabilities.

  • The Event Stream model along with ML model can be used for predictive analysis of live streams of data.

  • Amper’s AI powered platform automatically determines the best algorithm for a model based on the type of input parameter.

Amper Impact

  • Amper’s visual drag-drop development environment enables quick design of ML model to generate predictions.

  • Visual representation of the machine learning algorithm and its outcomes, facilitates the user to choose the best ML model.

  • The prediction intelligence of the model is improved through continuous learning and re-training.

  • When a ML model is re-trained, the Confusion Matrix in Amper forecasts the accuracy of the prediction of the trained model.

  • Third Party integrations can be done with ease in Amper with the help of API’s.

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Customer stories