One-click deployment to any cloud platform.
Choose commercial cloud of your choice.
Reduce model training and automation time.
Scalable implementation of ML algorithms.
Avoid dev-ops and data engieer costs.
Our scalable AutoML framework sits at the core of our AIaaS soltion providing solutions to any classification or regression problem on tabular data. Application extends to several market segments such as finance, telecommunications, automotive, medicine, manufacturing, etc.
Our Auto-ML platform provides a comprehensive model training and automation services along with bias and explainable AI and meets the current AI EU act requirements.
Framework deployed on a cloud platform can scale up the execution in real time with the addition of added nodes.
The complex process of model automation is simplified for the end user and made available via a few mouse clicks.
Incorporates state-of-art model agnostic methods applied before model training to help highlight most relevant features.
Continously monitor the steady drift in models features using state-of-art statistical measures and their affect on the trained model.
Quantitative measures for model drift and timely generated alarms for model retraining to ensure monetary contribution of the use-case.
Comprehensive list of model fairness indicators to keep track of model bias against protected features such as gender, postal area. see examples.
Post-hoc analysis of contributing model features and their collective affect on final model prediction. see examples.
We have developed several use-cases based on our automated machine learning framework, following are few highlighted ones.
Profound techniques to measure customer satisfaction on various touch-points along with the services a company provides to its customers.
Optimization of campaign activities by precisely segmenting customers for target offers, to increase convergence for x- and upselling activities.
Performance measurement of various customer touch-points w.r.t customer journeys and optimization techniques to push selected channels.
Identification of potential churners in due time to maximize retention efforts and give actionable insights on factors governing churn, e.g., bad customer experience.
Understanding the choice of the customers among the different product offerings und according provide a scoring for each product per customer to boost cross-selling.
Use of telemetry data to perform automated anomaly detection, diagnosis, and prediction by making use of our automated machine learning framework.
