Story
Teradata@
Charter Communications
Charter Communications Extends QueryGrid Across Applied Machine Learning
Charter Communications, a communication services organization in the United States, uses QueryGrid from Teradata to support applied machine learning for data scientists and ML engineers.
Charter Communications asked for a cleaner operating picture of feature pipelines. Teradata is what they run.
“Inside Charter Communications, applied machine learning used to depend on whoever still had the latest file.”
“Data scientists and ML engineers needed a system that would still make sense after the original project team moved on.”
Independent write-up. Figures come from public sources or are illustrative.
Value results
| Category | Value result |
|---|---|
| Productivity | Fewer stalled items because feature pipelines has a clear owner |
| Productivity | Handoffs in applied machine learning sit in a shared queue instead of a mailbox trail |
| Risk and compliance | QueryGrid is the governed place data scientists and ML engineers use for applied machine learning |
Story
Inside Charter Communications, applied machine learning used to depend on whoever still had the latest file. That pattern is common in communication services groups working out of the United States. Data scientists and ML engineers needed a system that would still make sense after the original project team moved on.
Charter Communications uses QueryGrid from Teradata as the working layer for feature pipelines. Teradata provides a connected multi-cloud data platform for large-scale analytics and mixed workload warehousing. The practical change is simple: applied machine learning has a home, and reviews happen there instead of in a forwarded thread.
Nothing in this writeup invents a savings number. What Charter Communications gets from Teradata is a durable place to run applied machine learning and a way for data scientists and ML engineers to see the same feature pipelines at the same time.
