A
A
AI Wiki
Gradient PlatformDocsGet Started FreeContact Sales
Search…
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
Powered By GitBook
Automated Machine Learning (AutoML)
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 evaluation metric criteria have been determined:
  1. 1.
    Data pre-processing
  2. 2.
    Data partitioning
  3. 3.
    Feature extraction
  4. 4.
    Algorithm selection
  5. 5.
    Training
  6. 6.
    Tuning
  7. 7.
    Ensembling
  8. 8.
    Deployment
  9. 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 transfer learning, neural architecture search, and other toolsets to determine the best performing model.
Topics - Previous
AUC (Area under the ROC Curve)
Next - Topics
CI/CD for Machine Learning
Last modified 2yr ago
Copy link