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...
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.
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.