Practical approach to Machine Learning
Zunō.predict was built ground-up to address these very real challenges. With proprietary algorithms such as SignalFactory and SignalFilter, Zunō.predict automates feature engineering and works very well in dynamic environments.
Rethinking every part of the model-building lifecycle
API-first design – specifically to address integration complexity.
Automate the full model-building lifecycle
Addresses the Complete Machine Learning lifecycle
The platform automates the full model-building lifecycle, including data preparation, feature engineering, and action-engine for predictions and recommendations, as well as self-learning capabilities to keep updating the predictive models.
Applying Zunō.predict in the real world
Zunō.predict is currently being used to solve a wide variety of problems across a wide variety of industries. From predicting estimated time of pickup and delivery for a logistics provider to helping debt collectors prioritize borrowers who are likely to repay, Zunō.predict is a versatile solution that can be customized to meet your unique needs.
What makes it tick?
Under the hood, Zunō.predict’s core is the SignalFactory and SignalFilter suite of proprietary algorithms. SignalFactory builds potentially thousands of signals hidden in the data using algorithms, representing the complete universe of potential hypotheses in the data. SignalFilter then filters the most powerful signals to build a model. This approach ensures that complex non-linear interactions between signals are also picked up. A power user can still build more complex models by ignoring certain signals or by considering only a subset of the data.
Zunō.predict is completely containerized and can be deployed anywhere. Use it on Cognida’s cloud, in an on-premise VM, or in your Kubernetes Cluster.