Catalog
concept#Machine Learning#Artificial Intelligence#Data#Software Engineering

Deep Learning

Learning paradigm using deep neural networks for automatic feature extraction and prediction.

Deep learning is a subset of machine learning that uses multi-layer neural networks to learn hierarchical representations from data.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

Data platform (data lake, feature store)CI/CD for model training and deploymentMonitoring and observability tools

Principles & goals

Data quality affects outcomes more than model size.Use pretrained models as basis for transfer learning.Ensure reproducibility and versioning of data and models.
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Bias in training data leads to unfair predictions.
  • Overfitting due to insufficient validation.
  • High operational costs due to repeated retraining.
  • Use pretrained models and transfer learning for efficiency.
  • Version data, code and model artifacts.
  • Early monitoring and automated testing of models in production.

I/O & resources

  • Representative labeled training data
  • Compute resources (GPUs/TPUs) and storage
  • Domain knowledge for labeling and evaluation
  • Trained model artifact
  • Evaluation metrics and test reports
  • Deployable inference components

Description

Deep learning is a subset of machine learning that uses multi-layer neural networks to learn hierarchical representations from data. It enables state-of-the-art performance in vision, language and speech by training large models on massive datasets. Practical use requires careful model design, data engineering and substantial compute resources.

✔

  • State-of-the-art performance in many perceptual tasks.
  • Automatic feature extraction reduces manual effort.
  • Large community and broad ecosystem of tools.

✖

  • Requires large labeled datasets for good generalization.
  • High compute and memory requirements for training.
  • Models are difficult to interpret.

  • Accuracy

    Portion of correctly classified examples; basic performance metric.

  • Latency per request

    Average response time for inference requests in milliseconds.

  • Throughput (inferences/s)

    Number of concurrent inference requests the system can handle.

Image classification in research

Research demonstrates how convolutional networks achieve leading accuracies in image tasks.

Transformer models for translation

Transformer architectures enable powerful translation and language models via self-attention.

Medical image analysis

Deep learning is used to assist diagnosis from radiological images but requires strict validation.

1

Define problem, select metrics and success criteria.

2

Data acquisition, cleaning and annotation.

3

Model selection, training, validation and hyperparameter tuning.

4

Optimize for inference and deploy to production environment.

⚠️ Technical debt & bottlenecks

  • Unversioned datasets and inconsistent training pipelines.
  • Ad-hoc deployment scripts instead of reproducible CI/CD processes.
  • Lack of test coverage for model changes and metric regressions.
data-qualitycompute-infrastructuremodel-interpretability
  • Using deep learning on extremely small datasets without transfer learning.
  • Automated decisions in sensitive contexts without auditing.
  • Ignoring sources of bias in training data and labels.
  • Overreliance on metrics without domain-specific assessment.
  • Underestimating the costs of continuous retraining.
  • Failing to distinguish correlation from causation.
Understanding of neural networks and optimizationData preprocessing and feature engineeringPractical experience with frameworks (PyTorch/TensorFlow)
Accuracy and generalizationScalability of training and inference infrastructureLatency and availability requirements in operation
  • • Limited availability of labeled domain data.
  • • Budget and energy constraints for training runs.
  • • Privacy and compliance requirements (GDPR etc.).