The End-to-End Data Workflow with Explorium

When doing things by hand goes wrong

Building your machine learning models by hand isn’t a problem when you’re working on smaller models or using smaller datasets, but they can be difficult to expand and manage as needs change. One of the common roadblocks data scientists and organizations face when building new ML models and data pipelines is being able to scale their projects.

Doing things manually is not inherently bad when it comes to data science, but like with everything, there’s a time and a place for it. For example, let’s say you’re running a small model — one that has a limited number of features and a narrow scope — with only internal data. In that case, you can probably handle the heavy lifting yourself. But what happens when you need external data? Or what if you need to distill features from a massive dataset? What you need is a way to optimize your data workflow at every step. Let’s see how Explorium can fit in to help with every phase of your data pipeline and optimize your efforts.