SKYL Platform

turning complex data into actionable intelligence

machine learning projects are very challenging endeavors

Disparate data-sources

Too many data sources and cumbersome data preparation processes require huge amount to time and effort

Infrastructure is time-consuming

Identifying right infrastructure and setting it up requires expertise and is time-consuming

Poor visibility

Due to the experimental nature of the ML project, visibility is usually poor until the very end of the project

Weak collaboration

Due to the lack of a unified platform, collaboration among product managers, data engineers, data scientists and DevOps engineers is very difficult


solving the machine learning problems with ease

Guided workflow

Guided process steps to automatically upload data from many sources, clean the data and label them efficiently

Abstracted complexity

Data management, training, testing and deployment can be done by product managers, with minimal dependency on data engineers and DevOps engineers

Easy segregation

Segregate train and test data set with ease and train the model faster

Scalable hosting

Scalable storage for data and run-time computational processing needs on the cloud

Rich visualization

Identify feature sets quickly, spot errors, outliers and hidden features, visualize data adequacy and readiness for models

Seamless integration

Seamless integration with external systems for data pull and model consumption

Ready to get started?