Auto ML - Finding the best model for your application

Both AI and Deep Learning applications are powered by algorithms, data, and their respective parameters, which operate in sync for optimum performance. The choice of algorithms, data processing and parameterisation – also known as hypertuning – and their subsequent interactions are carried out independently by Trendskout’s AutoML. This increases the entire process: from conceptualisation to delivery of an efficient AI and Deep Learning application.

Discover the importance of algorithm choices, data processing and algorithm parameters and how they interact in Automated Machine Learning.

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.

Algorithm choice

There are plenty of algorithms available for Artificial Intelligence, Machine Learning and Deep Learning. Popular examples include neural networks, gradient descent trees and support-vector machines or SVMs. It is not always straightforward to select the right algorithm for a particular business case. Every type of algorithm – take neural networks as an example – consists of different subtypes, increasing the challenge to make the appropriate choice of algorithm.

Trendskout evaluates each and every algorithm available and selects the most suitable approach for your selected application and data set.

Data processing

Selecting the right algorithm does not suffice to be successful. Algorithms need to have the right data to train themselves, make predictions and identify links. The shape of the data is of paramount importance: data processing will be essential before the algorithm can start analysing and interpreting those data. The options for data processing are virtually unlimited and include data merging, transformation, generation of derivatives and variations, and denormalisation.

Choices about what operations should be carried out on which data, can get complicated and depend on the selected application and algorithm.

Algorithm parameters

Every AI, Deep Learning or Machine Learning algorithm implies a range of options, which are dependent on the type of algorithm (e.g. a neural network) and its subtype. Each algorithm subtype presents a host of different options, too: neural networks, for example, can shape themselves in a myriad of different ways. Just consider the possible numbers of neurons and layers and the way they are connected and exchange information. The same complexity goes for other types of algorithms. Setting the right parameter configuration proves time and time again to be equally crucial for the accuracy and performance of your Artificial Intelligence, Machine Learning or Deep Learning application.

Trend skout Auto ML

Trendskout contains an additional Deep Learning motor or AutoML, designed to make the right choice in a world of infinite options. This AutoML relies on an intelligent combination of machine learning, artificial intelligence and deep learning to search for the ideal combination between data processing, the algorithm and relevant parameters. This selection process is partly based on genetic algorithms that use the biological concept of ‘survival of the fittest’.

Let us expand briefly. Using an iterative update process, our software executes combinations and simulations. Every iteration uses data about quality, performance and other relevant information to improve the results for the next iteration, based on an intelligent variation of the data processing, algorithm and parameters mentioned above. This process is similar to evolution by means of natural selection, which relies on genetic selection, mutations and crossover to continually improve results.

This AutoML engine drives Trendskout’s artificial intelligence, machine learning and deep learning, ensuring the use of the best possible AI model for your application. The successive iterations are performed at a rapid pace, allowing you to achieve results faster and use AI, ML and Deep Learning in a scalable way.

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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...