Data Classification in the Trendskout AI Platform

Applications for Artificial Intelligence and Deep Learning are defined by three key variables: data processing, algorithms and their respective parameters. They must be combined and evaluated in sync to form an accurate and high-performance framework. Together, they decide the quality of the resulting model for AI and Deep Learning.

The crucial choices and cooperation between the algorithm, data processing and parameterisation – also known as hypertuning – is carried out independently by Trendskout’s AutoML. This speeds up the entire process, from application definition all the way to delivery of a high-performance AI and Deep Learning model.

Application Examples

Powerful Cloud-AI Platform

In addition to specific modules for manufacturers, the Trendskout AutoML platform also offers many possibilities for IT teams to quickly set up AI applications, Powered by our unique double AI low and easy to use UI that fully automates data transformations, algorithm selection and hypertuning.

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Classification is one of the available Trendskout AI Flow analysis-functions.



How does this work technically?

Step 1: Training the algorithm

During the first step, a set of training data is provided with labels, or classes, on which the algorithm can rely on to learn. Labeling the training data is crucial. You can compare this with a person who learns how to label data correctly. This person also needs a first set of labeled sample data. In contrast with a human, a data classification algorithm will be able to learn the correct insights much faster and will be able to detect more subtle relationships and will also be able to process much larger amounts of data, which improves the quality of the detected relationships. Apart from adding a label, no other input is required within Trendskout to train the classification algorithm.

Through “Feature Selection” and “Feature extraction” algorithms, which are executed completely autonomously, Trendskout independently detects which set of variables – eg. columns, pixels … – must be used in the classification model.

During training, Trendskout evaluates various algorithms, algorithm parameters and data transformations. To evaluate this, different quality scores are calculated by running the model on an unused part of the training data and testing whether the discovered relationships are correct. Based on the result of this evaluation, Trendskout will independently try new combinations of algorithms, parameters and data transformations. This part of the training step is called hypertuning. This entire process is started in Trendskout via a simple click on the Run / Train button and is done without user intervention.

Step 2: Real-time classification

The best classification model is then used in the second step; classifying new data in real time. Clicking on Deploy will open the classification model for a data input method chosen in the Connect step. This can be based on API, Plugin, database or others. Each time, Trendskout will classify new data in these input sources, based on the classification model, and perform the selected actions in the Automate step, e.g. calling an external system, sending an email, showing a dialogue …

In this second phase, Trendskout will also adjust and evaluate the previously prepared model. As soon as a different algorithm / data transformation / parameter combination yields a better model, the previous model is replaced. This guarantees the best result, even when the properties of your data change.

Data Classification + Trendskout

The Trendskout automated machine learning platform contains numerous classification algorithms that the user can link to input and automation steps in an AI flow in an accessible manner via a drag & drop interface. All data transformation, hypertuning, algorithm selection and the management of all GPU / TPU Cloud Computing are fully managed in the background.

This makes the deployment of classification applications in your organization a lot more efficient and you can experiment freely and without worry with possible applications within your organization.

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Every AI or Machine Learning project is unique: diverse data sets with different variables, integrations in or with existing software or hardware and different expectations and goals to be pursued. The decision as to how a business case should be set up technically in practice is an important factor in its ultimate success. In this...