Learning Algorithm

The choice of learning algorithm is a central decision in an ML project. For structured tabular data, gradient boosting methods like XGBoost have proven robust. For images, Convolutional Neural Networks dominate, for text and sequences Transformer architectures, for sequential decision tasks Reinforcement Learning. Every algorithm has its strengths in certain data types and application fields.

Learning algorithms also differ in data needs. Classic statistical methods work with a few hundred data points, deep learning models often require millions. These requirements have to be matched against available data and compute power before an algorithm is chosen.

Pimcore selects learning algorithms application-specifically. Product data classification uses gradient boosting methods on structured attributes, image tagging Convolutional Neural Networks, text generation Transformer-based LLMs. The Agent SDK abstracts the algorithm choice, so users can build applications without making ML architecture decisions themselves.