The Challenges with Data for Machine Learning
Data scientists at consumer-facing organizations know the frustration of incorporating external data to build or improve the performance of machine learning models. The time needed to acquire 3rd party data, sort through it for relevant features, and then join it to existing data sets detracts from model optimization efforts and delays production.
At the same time, business leaders, who know that data and machine learning are critical in today’s fast-moving digital consumer landscape, feel that their investments in these areas are not delivering as expected. Whether due to a lack of internal data or the long time to model production, machine learning is not providing the timely and accurate predictions about customer behavior that are needed to make a business impact.
In this White Paper…
Read how the evolution from data to machine learning-ready Signals changes the game, and makes it easier and quicker to build better models. This white paper includes information about:
- What are Signals, and how are they created?
- What makes Signals easier to use than external data?
- How do Signals improve the performance of machine learning models and business outcomes?
- What types of machine learning models can Signals enhance?