How it works
Our Automated Machine Learning Platform (Auto-ML) empowers our clients to solve complex business problems. Auto-ML has the capability to continuously provide you with better output and predictions. Moreover, we offer optimal integration with your systems and can develop tailor-made models to cater to your needs. As our platform can be applied to various use cases across industries, it enables anyone to generate maximum value from their data.
Auto Machine Learning
Adaptable to multi-input and multi-output, flexible architecture that auto generates and optimizes deep neural network models
Optimise model architecture automatically to achieve best performing model
End to End Machine Learning Platform
Accelerates the process to get production-ready ML models
Train models to take in relevant inputs(data) and further investigate
the model training with appropriate visualization and can also be further trained for better consistency
It can be configured for on-premises deployment
Smooth UX and Intuitive UI
The smooth UX and intuitive UI makes Auto-ML an easy-to-adopt tool which is user-friendly to a wide range of target in terms of technical skills – ranging from less tech-savvy people to data scientists.
Upload schema and data
25%
Generate all possible ML models
50%
Choose the best performing model
75%
Predict the results using the best model
100%
Six types of prediction models
Regression
Determine the strength and character of the relationship between one dependent variable and a series of other variables. The output is a quantifiable value.
Classification
Predict the result through systematic grouping of observations into categories. E.g., Risk Classification, Text Classification, Intent Recognition.
Computer Vision
Segmentation from image, video and visual input. E.g., image annotation, numbers and sentence classification.
NER (Name Entity Recognition)
Find the most important wording element in the data and then classification such as part of speech.
Sentiment Analysis
Predicts the natural language processing such as text analysis to study affective states and subjective information.
Recommendation
Predicts the similar items or similar users for a particular user based on their historical behavior.
