It’s hard to find an IoT platform that doesn’t claim a successful solution of typical IoT-related machine learning tasks, such as predictive maintenance. However, in many cases it just means selling professional services of vendor’s analysts rather than a product.
AggreGate is very practical when it comes to machine learning. It’s an instrument allowing data scientists to drill into time series streams and extremely large datasets in order to mine valuable knowledge.
Technically speaking, it uses both supervised and unsupervised learning methods to solve three major objectives:
|Classification of values or datasets|
Trainable units that perform actual learning and scoring have so-called hyperparameters used by data scientists to fine-tune algorithm behavior. Combined with workflows, the machine learning module is a one-stop tool for predicting failures and optimizing operation of both physical assets and business services.
There are well-known algorithms under the hood of machine learning module: linear regression, support vector regression, REP decision tree, random forest, multilayer perceptron (feedforward neural network), naive Bayes classifier, and many more.
Trainable units address AggreGate unified data model and therefore interact with any module, device and data source within the platform. The units can also work in incremental learning mode, getting on-the-fly model updates once new data comes in the form of device or system events.
Scripting in R and Python enhances functionality of the data filtering stage and the learning process itself.