With the increasing adoption of Artificial Intelligence (AI) by companies around the world across all industries, developing a strategy for machine learning is imperative to gain a competitive advantage. A key component of this strategy is the data used to train machine learning-based solutions.
Machine learning- a form of AI that uses large datasets to teach computers how to respond to and act like humans – allows 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?
This white paper was developed to share some guiding principles about the quantity and quality of training data needed to enhance your machine learning program.
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 white paper today to learn more about this important piece of your machine learning strategy.