Discover new approaches to time management, teamwork and collaboration, client service and business forecasting. Mine troves of inert customer data to reveal sales pipeline bottlenecks, build more in-depth personas and discover opportunities for upsales. MLaaS empowers Enterprise to capitalize on opportunities that were previously undiscovered.
MLaaS.net is the only system that brings together a full spectrum of AI algorithms including:
Convolutional Neural Networks
Restricted Boltzmann Machines
Probabilistic Graphical Models
The following is an example of how MLaaS works. For the sake of the non-technically inclined, it has been presented in the simplest terms possible.
Machine learning systems are made up of three major parts:
Keeping in mind that this is a very basic example of MLaaS’s capabilities, let’s imagine that a manufacturing company wants to identify the optimal amount of training required in order to get a production line up to a certain level of efficiency.
Everything starts with the model, or the prediction that MLaaS will use. In most cases, the model initially has to be provided by a human. In our case, the production manager will tell the MLaaS model to assume that training for 10 hours will lead to an optimum level of efficiency.
The model itself requires parameters in order to make calculations. In this example, the parameters are the hours spent training and the production level achieved. Imagine that the parameters are something like this:
Now that the model is set, real-world information is entered. Our production manager, for example, might input four efficiency scores from different employees, along with the the duration of training they received.
As it turns out, the scores don’t match the model, in this example. Some are above or below the predicted trend line.
The initial data given to MLaaS is called a “training set” or “training data” because the system uses it to train itself to create a better model.
MLaaS looks at the scores to determine how far off they were from the model. It then adjusts the initial assumptions, for example, by changing the model as such:
By making very small adjustments to the parameters, MLaaS refines its model.
The system is run again, this time with a new set of scores, which are compared against the revised model. Ideally, the scores will be closer to the prediction:
These results are better, but not perfect. So, the learner will once again adjust the parameters and reshape the model. Another set of data will be inputted, and the cycle will be repeated until the objective is revealed, telling our production manager exactly how many training hours are optimal for reaching a certain efficiency target.
So now you have an idea of how MLaaS works. Sounds complicated, right?
The good news is, despite the complexity of the technology, the client experience is amazingly simple. To unlock the potential in your data, you need only follow these steps:
Whether you’re looking to identify hidden sales opportunities, detect fraudulent activity or optimize inventory management processes, a clear objective is the first step.
Like most things involving computers, machine learning analysis is only as good as the data provided. Sightline will work with you to transfer and properly label your data to optimize results.
With steps one and two complete, Sightline’s technicians will set to work implementing the MLaaS system to create a model based on your data.
Once the model is trained and producing accurate results, MLaaS is integrated with whatever system you have, be it email, database or claims files. MLaaS is now ready to turn your data into actionable intelligence.