With the increasing adoption of Artificial Intelligence (AI) by companies around the world across all industries, developing a strategy for machine learning and understanding your need for training data and machine learning datasets is now an imperative for gaining competitive advantage.
Machine learning- a form of AI that uses large datasets to teach computers how to respond to and act like humans – is allowing businesses to optimize operations, deliver better customer experiences, enhance security and more.
However, in a recent study from Oxford Economics and ServiceNow, 51% of CIOs cite data quality as a substantial barrier to their company’s adoption of machine learning.
When it comes to your machine learning strategy, have you considered the amount and type of machine learning datasets you’ll need to effectively train your machine learning models?
We created this whitepaper for business executives and product managers embarking on—or looking to improve—their machine learning initiatives, to share a few guiding principles about the quantity and quality of training data you are likely to need.
In it, we cover:
- Why machine learning requires a high volume of data
- The importance of high-quality data
- Data sources to consider
Download our whitepaper today to learn more about this key component of your machine learning strategy.