A common roadblock that data scientists and organizations face when building new machine learning models is the ability to scale their projects.
Building machine learning models manually is fine when working with smaller datasets, but can become difficult when needs change and there is need for expansion. When incorporating external data, or distilling features from massive datasets, manual model training will create a bottleneck. The data workflow needs to be optimized at every step. Explorium can help with every phase of the data pipeline and optimize your data acquisition and model training efforts.
