The AI market is booming, and data scientists who have advanced the craft of combining data and code to solve complex, important problems must be given substantial credit, as do line-of-business leaders who have wisely chosen to invest in a growing method of delivering predictive analytics.
However, the majority of research and attention related to AI concentrates on complicated machine learning techniques and refining algorithm code. It’s imperative to remember that the data used to train algorithms can be much more impactful to predictive modeling accuracy than the machine learning technique used to build the model.
Highlights
How data quality, breadth, and depth are crucial to building accurate predictive models
How data enrichment and feature engineering benefit AI in fintech
Why the data-centric AI philosophy is the way forward for the expanding world of AI
