Catalog
concept#Machine Learning#Architecture#Data#Software Engineering

Transformer

A neural architecture paradigm based on self-attention for sequential and multimodal data. Common foundation for large language, vision and multimodal models.

Transformers are a deep-learning architecture based on self-attention that enables efficient processing of sequential data.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Advanced

Technical context

PyTorch for model implementationTensorFlow / Keras as alternativeHugging Face Hub for model distribution

Principles & goals

Use self-attention as the central representation mechanism.Scaling via depth and width enables performance improvements.Pretraining + fine-tuning as preferred development cycle.
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Overfitting on small datasets without regularization.
  • Amplification of bias and toxic patterns from training data.
  • High operational and energy costs in production.
  • Prototype with smaller models, then scale training.
  • Use regularization and data augmentation to reduce overfitting.
  • Establish monitoring for performance, cost and fairness in production.

I/O & resources

  • Training corpus (text, image, audio)
  • Tokenization and preprocessing pipeline
  • Compute infrastructure (GPUs/TPUs) and storage
  • Pretrained or fine-tuned model weights
  • Evaluation results and metrics
  • Deployed inference API or model artifacts

Description

Transformers are a deep-learning architecture based on self-attention that enables efficient processing of sequential data. They replaced recurrence in NLP and power large-scale models for language, vision, and multimodal tasks. Transformers enable parallelization and long-range context modeling but require significant compute and large datasets.

✔

  • Efficient parallelization during training.
  • Good modeling of long-range dependencies.
  • Universal template for multiple modalities (text, image, audio).

✖

  • High compute and memory requirements for large models.
  • Requires extensive and often costly training data.
  • Interpretability of internal representations is limited.

  • Perplexity

    Measure of predictive quality for language models; lower is better.

  • Throughput (tokens/s)

    Indicates processing speed during training or inference.

  • Latency (ms)

    Time to output during inference, relevant for production.

BERT (example)

Bidirectional transformer for many NLP tasks, pretrained and widely used.

GPT family (example)

Autoregressive transformer models used for text generation and dialog systems.

Vision Transformer (ViT)

Application of the transformer principle to image patches for image classification.

1

Define requirements and target task, evaluate architecture variants.

2

Build data pipeline: tokenization, augmentation, splitting.

3

Use pretraining or transfer learning, optimize hyperparameters.

4

Perform evaluation, robustness checks and staged deployment.

⚠️ Technical debt & bottlenecks

  • Monolithic, unoptimized models hinder updates.
  • Lack of reproducibility in training pipelines.
  • Insufficient model versioning and artifact management.
Memory bandwidthGPU/TPU capacityData preprocessing
  • Using a large transformer for small trivial tasks leads to overkill.
  • Missing anonymization of training data containing sensitive content.
  • Blind fine-tuning without evaluation on domain specifics.
  • Underestimating infrastructure costs when scaling.
  • Underestimating complexity of hyperparameter tuning.
  • Relying on benchmarks without realistic production data.
Deep knowledge of neural networks and attention mechanismsML engineering skills for training and deploymentData engineering for preprocessing and quality assurance
Scalability for large datasetsLong-range context modelingParallel training capability
  • • Availability of large, high-quality datasets.
  • • Budget for compute resources and infrastructure.
  • • Compliance and data protection requirements for training data.