The Data Dilemma for Data Scientists
Data Science is rapidly changing the modern enterprise, redefining what it takes to be competitive across most every industry. The competition for data scientists is fierce with every organization competing for scarce skills. One of the challenges to getting more from existing resources is the inefficiencies of working with data. Some estimates show data scientists spending 60% or more of their time wrangling data.
Data Scientists can access live cloud data through an AtScale semantic layer using simple python scripts. This approach simplifies data pipelines and can accelerate feature engineering. Data teams define views of live cloud data that are optimized for model ingest including creation of calculated metrics, time-relative metrics, and custom dimensions. By employing virtualized views of data, data movement and ETL are minimized. Furthermore, pipelines are protected from changes to underlying data.
The semantic layer can also provide a path for publishing model results back to the business for consumption in existing dashboards and reports.
AtScale helps data teams simplify and harden ML data pipelines while providing a path to publish model outputs back to the business.
The AtScale AI-Link Advantage