person Author: Process Fellows

• Amount of data: A linear regression model requires significantly less data than a “deep neural network”
• Faulty or noisy data (e.g. sensor data) require pre-processing.
• Data structure in combination with the problem to be solved has an impact too:
  • Tabular data → Decision trees or gradient boosting (e.g. XGBoost)
  • Images → Convolutional Neural Networks (CNNs)
  • Texts → Transformer models (e.g. BERT, GPT)
  • Time series → Recurrent neural networks (RNNs) or Long Short-Term Memory (LSTMs) / Gated Recurrent Units (GRUs, similar to LSTM but less computational power needed)
• Data availability:
  • Real time applications (e.g. autonomous driving): low latency, optimized neural networks
  • Batch processing (e.g. recommendation system for Netflix): More time for complex models, deeper architectures are possible.
• Feature Dimensionality: Models for high-dimensional data require special techniques for reduction, e.g.
  • image data (millions of pixels per image) → CNNs with feature extraction.