Machine learning finds and uses insights in data that are difficult or impossible to identify by humans and are tricky to solve using conventional algorithmic programming. Problems able to be solved are wide-ranging and might be, for example, scheduling difficulties, maintenance issues, process problems, product quality issues or even worker related.
There are broadly four main ways machine learning can help. The first is detection which is taking in, usually complex, input data and determining if something has just happened. For example, you might detect a motor has failed in a predictable way by processing its vibration.
The second is classification where you want to know the type of something that has just happened. For the motor, you might want to know if it’s running at high, medium or low speed based on a non-contact rotational proximity sensor.
Related to both detection and classification is anomaly detection. This is knowing something isn’t behaving as normal. For a motor, this might be detecting a motor has failed in an unpredictable way by processing its vibration.
The final capability is prediction which is where a pattern in the data means something is about to happen. In the case of the motor, a subtle sound or vibration change might signify the motor is about to fail.
In terms of your business, the previously mentioned business problems need to be mapped onto detection, classification or prediction. We help you with this as part of the machine learning development process.