5 Questions to Ask When Choosing an Anomaly Detection and Prediction Strategy

Following the increasing pervasiveness of the Industrial Internet of Things (IIoT), machine learning and artificial intelligence (AI), anomaly detection and prediction is the most natural application of these technologies. The question is no longer whether businesses need anomaly detection and prediction, it’s a question of how they should implement it.

Implementation is the tricky bit. With so many approaches, machine learning algorithms and outcomes to consider, the process of choosing the right one is far from easy.

So, how do you know which approach is the best fit for your business? We recently created a handy checklist that might help you navigate these murky waters. This checklist can help you:

  • Learn about the most common approaches available today
  • Identify the five most important questions to ask yourself when choosing an approach
  • Narrow your list to the approaches that best fit your business

Download the checklist today and find out which approach is best for you.


About DataRPM

DataRPM is an award-winning predictive analytics company focused on delivering the next generation predictive maintenance solutions for the Industrial IoT. DataRPM platform automates data science leveraging the next frontier in machine learning known as meta-learning, which is machine learning on machine learning. The platform increases prediction quality and accuracy by over 300% in 1/30th the time and resources delivering 30% in cost savings or revenue growth for business problems around predicting asset failures, reducing maintenance costs, optimizing inventory and resources, predicting quality issues, forecasting warranty and insurance claims and managing risks better.

Industry Recognitions and Awards

Download the Checklist