Going from prototype to production is perilous when it comes to artificial intelligence (AI) and machine learning (ML). However, many organizations struggle moving from a prototype on a single machine to a scalable, production-grade deployment. In fact, research has found that the vast majority—87%—of AI projects never make it into production. And for the few models that are ever deployed, it takes 90 days or more to get there.

I’m sure many of you have heard of our Machine Learning Toolkit (MLTK) app and may even have played around with it. Some of you might actually have production workloads that rely on MLTK without being aware of it, such as predictive analytics in Splunk IT Service Intelligence (ITSI) or MLTK searches in Splunk Enterprise Security.

There is a lot of buzz in the world of machine learning (ML) and as a layperson it can be hard to keep up with it all. Therefore, we decided to write down some of our thoughts and musings on how we are approaching ML at Netdata. We’ll touch on the current state of applied ML in industry in general, and zoom in on ML in the monitoring industry.

Hearing loss is a rapidly growing area of scientific research as the number of baby boomers dealing with hearing loss continues to increase as they age. To understand how hearing loss impacts people, researchers study people’s ability to recognize speech. It is more difficult for people to recognize human speech if there is reverberation, some hearing impairment, or significant background noise, such as traffic noise or multiple speakers.