Feature engineering has played a pivotal role in machine learning. But with deep learning’s ability to learn patterns directly from raw data, the role of manual feature engineering is being redefined.
This raises a key question for practitioners and researchers alike: to what extent does feature engineering remain critical in the deep learning era?
In conclusion, feature engineering is not dead; it has evolved and now complements deep learning approaches rather than being fully replaced by them.
Instead of manually designing features, practitioners now focus on structuring and augmenting data before it enters the model.
Where Traditional Feature Engineering Still Matters:
Despite the rise of deep learning, classical feature engineering continues to play a critical role in many scenarios.
Tabular Deep Learning: Architectures such as TabNet and FT-Transformer have expanded the use of deep learning on structured data. However, raw tabular inputs still require careful preparation.
For example:
- Categorical variables must be appropriately encoded.
- Missing values still call for thoughtful imputation strategies.
- Domain-specific transformations can provide models with a significant performance advantage.
Time Series, NLP, and Hybrid Systems:
Feature engineering continues to provide value across domains such as time series, natural language processing, and hybrid systems.
- Utilization of rolling averages and lagged variables to capture temporal dependencies in time series data.
- Integration of TF-IDF and other statistical signals within hybrid NLP architectures.
- Incorporation of external knowledge-driven features to augment dataset richness.
Evolving Roles in Modern Machine Learning:
Positional Features and Representation Learning
In domains such as language and spatial data, positional encoding is a prime example of modern feature engineering. Since transformers do not rely on recurrence or convolution, they require explicit positional signals to capture sequence and spatial relationships effectively.
Data Augmentation as the New Feature Engineering:
In self-supervised learning, feature engineering often takes the form of data augmentation and structuring for specific learning objectives. Consider contrastive learning: the model’s task is to recognize that two augmented views originate from the same source. In this case, the augmentation strategy itself becomes a form of feature engineering.
In masked language modeling (like BERT), we decide which words to hide and how to hide them. Those choices shape how the model learns—so in a way, we’re still doing feature engineering, just at the task-design level.
The Evolving Feature Engineering Mindset
In modern machine learning, feature engineering centers on:
- Designing input representations that reflect appropriate inductive biases
- Creating augmentations that highlight meaningful invariances
- Defining pretext tasks that foster the development of robust embeddings
Strategic Data Shaping Beyond Preprocessing:
In modern machine learning, feature engineering is evolving into data shaping—the practice of curating datasets to maximize learning efficiency, fairness, and robustness.
Bias Mitigation Through Feature Balancing
Consider a binary classification task where gender is unevenly distributed across classes. Instead of removing the feature or ignoring the imbalance, practitioners can rebalance the dataset (e.g., through re-sampling strategies) to ensure fairer and more reliable model behavior.Building Features at Scale:
Hugging Face Datasets: Enable streaming, tokenization, and transformation of massive NLP and multimodal datasets directly at the source, reducing the need for heavy local preprocessing.
Closing Insight:
- PyTorch Tabular: Provides high-level APIs for embedding categorical features, normalization, and deep learning–optimized tabular pipelines.
- Vertex AI: Enterprise-scale platform tools to manage feature freshness, dependencies, and reuse across teams.
- Feast: A production-grade feature store that manages versioned features across both training and inference environments.