// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Feature Engineering
The process of creating new input features for a machine learning model from existing raw data to improve its performance.
TECHNICAL DEFINITION
Feature engineering is the art and science of transforming raw data into features that better represent the underlying problem to predictive models, often involving domain knowledge, aggregation, or interaction terms.
BACKGROUND
Prompt engineering is the process of structuring natural language inputs to produce specified outputs from a generative artificial intelligence (GenAI) model. Context engineering is the related area of software engineering that focuses on the management of non-prompt contexts supplied to the GenAI model, such as metadata, API tools, and tokens.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Feature creation
- data transformation
- variable construction
USAGE NOTE
Effective feature engineering can significantly boost model accuracy and interpretability.
DEVELOPERS
Organizations developing technology related to Feature Engineering.
Tecton provides an enterprise feature platform that enables data scientists and ML engineers to build, manage, and serve features for machine learning models at scale.
Databricks offers a unified data and AI platform, including a Feature Store that allows teams to discover, share, and reuse features for training and serving machine learning models.
Google Cloud's Vertex AI provides a comprehensive ML platform that includes tools for data preparation, feature engineering, and a managed Feature Store for developing and deploying AI models.
AWS SageMaker is a fully managed service that helps data scientists and developers build, train, and deploy machine learning models quickly. It includes SageMaker Feature Store for creating, storing, and sharing features.
Azure Machine Learning provides an enterprise-grade service for the end-to-end machine learning lifecycle, offering tools for data preparation, feature engineering, and automated ML capabilities.
DataRobot offers an automated machine learning platform that includes automated feature engineering capabilities, helping users create high-quality features from raw data for model building.
Explorium specializes in automated feature discovery and data enrichment, helping data scientists automatically find and create new, impactful features from internal and external data sources for machine learning models.
While primarily known for large language models, datasets, and a model hub, Hugging Face provides libraries (like Transformers and Datasets) that are instrumental in feature extraction and processing text/data into suitable features for training advanced AI models.