There are plenty of open-source libraries available for machine learning modeling. We use several of these libraries to build and evolve what is known as an “artificial neuro network”. We build neuro networks for an array of applications. An obvious example of this is facial recognition. Facial recognition is one of the forefathers of neuro network engineering. As such, there are many inexpensive libraries available to achieve facial recognition in a third party application. However there are many niche scenarios where machine learning can create a large impact, but has not yet been created. This is where Webables steps in.
We are able to deploy a series of processes on your network or physical environment to watch and collect data for analysis. Once enough data is archived, we begin to process the data with a series of cyclical programming iterations. We organize the data with different recurring “trends” for each programming cycle. We allow the programming iterations to cycle until we have clearly defined trends in the company’s data. The following is an example of some of the trends we might find or be looking for
- Waste or anomalies in Cost of Goods
- Waste or anomalies in asset allocations
- Sales trends in relation to specific assets (locations, employees, markets)
- Internal theft/malfeasance
- Key Performance Indicators
With a real-time picture of your company and it’s specific performances, you’re able to make the impactful decisions that cut waste and contribute to growth. Though focused, the data mining process often reveals meta trends that were not intended but prove valuable.