Machine learning and predictive analytics work better together Posted on : Oct 31 - 2020

Artificial intelligence and machine learning, when combined with predictive analytics, allow companies and organizations to get the most out of their data.

Like many AI technologies, the difference between machine learning and predictive analytics lies in applications and use cases. Machine learning's ability to learn from previous data sets and stay nimble lends itself to diverse applications like neural networks or image detection, while predictive analytics' narrow focus is on forecasting specific target variables.

Instead of implementing one type of AI or choosing between the two strategies, companies that want to get the most out of their data should combine the processing power of predictive analytics and machine learning.

At the core of machine learning

Artificial intelligence is the replication of human intelligence by machines. This includes numerous technologies such as robotic process automation (RPA), natural language processing (NLP) and machine learning. These diverse technologies each replicate human abilities but often operate differently in order to accomplish their specific tasks.

Machine learning is a form of AI that allows software applications to become progressively more accurate at prediction without being expressly programmed to do so. The algorithms applied to machine learning programs and software are created to be versatile and allow for developers to make changes via hyperparameter tuning. The machine 'learns' by processing large amounts of data and detecting patterns within this set. Machine learning is the foundational basis for advanced technologies like deep learning, neural networks and autonomous vehicle operation.

Machine learning can increase the speed at which data is processed and analyzed and is a clear candidate through which AI and predictive analytics can coalesce. Using machine learning, algorithms can train on even larger data sets and perform deeper analysis on multiple variables with minor changes in deployment.

Machine learning and AI have become enterprise staples, and the debate over value is obsolete in the eyes of Gartner analyst Whit Andrews. In years prior, operationalizing machine learning required a difficult transition for organizations, but the technology has now successful implementation in numerous industries due to the popularity of open source and private software machine learning development.

"Machine learning is easier to use now by far than it was five years ago," Andrews said. "And it's also likely to be more familiar to the organization's business leaders." View More