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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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  1. Topics

Automated Machine Learning (AutoML)

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Last updated 5 years ago

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Applied AI can be time-consuming, resource-intensive, and challenging. Automated Machine Learning (AutoML) seeks to automate the many cumbersome and repetitive steps of the machine learning pipeline to make it easier to apply machine learning methods to real-world business problems.

These are the typical steps that can be automated after the target variable and criteria have been determined:

  1. Data pre-processing

  2. Data partitioning

  3. Feature extraction

  4. Algorithm selection

  5. Training

  6. Tuning

  7. Ensembling

  8. Deployment

  9. Monitoring

AutoML generally speaking is defined as the process of selecting the combination of algorithm and parameters that collectively produce the best performing model automatically.

AutoML is a technology that people with limited machine learning expertise (sometimes referred to as Citizen Data Scientists) can leverage to produce state-of-the-art models. Some AutoML tools are “drag-and-drop” or "no code" in that the user can simply upload a dataset and get a trained model. Other more advanced tools are used to free data scientists from the burden of repetitive and time-consuming tasks such as pipeline design and hyperparameter optimization -- but require expertise in machine learning.

AutoML leverages , neural architecture search, and other toolsets to determine the best performing model.

evaluation metric
transfer learning