Many people are confused about the specifics of machine learning and predictive analytics. Although they are both centered on efficient data processing, there are many differences. Machine learning is a method of computational learning underlying most artificial intelligence (AI) applications. In ML, systems or algorithms improve themselves through data experience without relying on explicit programming. ML algorithms are wide-ranging tools capable of carrying out predictions while simultaneously learning from over trillions of observations.
Predictive analytics is a term that refers to using machine learning to analyze business data and make predictions about future events. This is a powerful technique that is currently on the rise, specifically in the manufacturing industry. Companies are using predictive analytics to reduce costs, repair expensive machines before they break, and even to optimize schedules, resource consumption, and product quality. One important piece in implementing predictive analytics for manufacturing is the necessary data science effort of collecting, researching, testing, and processing data. The other main effort necessary is to incorporate the results that come out of the analytics stack into business systems that automate otherwise manual steps.
The new name came with a new focus and a suite of new products, based on the software-as-a-service (SaaS) model. The enterprise then gets insight and reports into potential security risks and incidents in their environment". We had to dig a bit to understand where AI fits into all this, as Anomali doesn't really play the artificial intelligence card. The company does say that as part of its threat intelligence platform service, it integrates machine learning algorithms for various functions such as automating searches for new domain registrations looking for those that can be considered suspicions and potentially vicious.
For all the promise of IT, the underlying infrastructure that most applications rely on today isn't all that smart. Most IT infrastructures issue a continuous stream of alerts and alarms that do more harm than good. The volume of noise being generated about minor events winds up making IT administrators inured to the point where they start to suffer from IT alarm fatigue. To help with the signal-to-noise ratio that IT administrators are now subject to, Perspica this week launched Incident Replay, a software-as-a-service (SaaS) application that combines machine learning algorithms and predictive analytics to identify issues and make recommendations on how to actually improve the performance and manageability of the overall IT environment. Perspica CEO Dan Maloney says Perspica creates a knowledge base from the data collected via machine learning algorithms.