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  • Artificial Intelligence Wiki
  • Topics
    • Accuracy and Loss
    • Activation Function
    • AI Chips for Training and Inference
    • Artifacts
    • Artificial General Intelligence (AGI)
    • AUC (Area under the ROC Curve)
    • Automated Machine Learning (AutoML)
    • CI/CD for Machine Learning
    • Comparison of ML Frameworks
    • Confusion Matrix
    • Containers
    • Convergence
    • Convolutional Neural Network (CNN)
    • Datasets and Machine Learning
    • Data Science vs Machine Learning vs Deep Learning
    • Distributed Training (TensorFlow, MPI, & Horovod)
    • Generative Adversarial Network (GAN)
    • Epochs, Batch Size, & Iterations
    • ETL
    • Features, Feature Engineering, & Feature Stores
    • Gradient Boosting
    • Gradient Descent
    • Hyperparameter Optimization
    • Interpretability
    • Jupyter Notebooks
    • Kubernetes
    • Linear Regression
    • Logistic Regression
    • Long Short-Term Memory (LSTM)
    • Machine Learning Operations (MLOps)
    • Managing Machine Learning Models
    • ML Showcase
    • Metrics in Machine Learning
    • Machine Learning Models Explained
    • Model Deployment (Inference)
    • Model Drift & Decay
    • Model Training
    • MNIST
    • Overfitting vs Underfitting
    • Random Forest
    • Recurrent Neural Network (RNN)
    • Reproducibility in Machine Learning
    • REST and gRPC
    • Serverless ML: FaaS and Lambda
    • Synthetic Data
    • Structured vs Unstructured Data
    • Supervised, Unsupervised, & Reinforcement Learning
    • TensorBoard
    • Tensor Processing Unit (TPU)
    • Transfer Learning
    • Weights and Biases
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  • Machine Learning Features and Feature Engineering
  • Feature Store

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  1. Topics

Features, Feature Engineering, & Feature Stores

Machine Learning Features and Feature Engineering

Features are individual independent variables that act as inputs in a machine learning system. Features are properties of a problem for which we would like to predict results. In simplistic terms, one column of a data set can be considered to be one feature. In a more real-world scenario, you would obtain training features from existing features using a method known as “feature engineering.”

Feature Store

Model-driven organizations are beginning to store features centrally in what has recently been termed a feature store. A feature store is a data management layer (the output of a data lake) that allows data scientists and data engineers to share and discover features.

Feature stores enable highly curated and consistent training datasets for machine learning. This layer aids in implementing full traceability along with compliance and scalability from data source to final outcome. The term was originally coined by Uber with the introduction of its Michelangelo machine learning platform.

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Last updated 5 years ago

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