AutoML Library

A AutoML Library is a software library that support AutoML tasks (machine learning workflows and model development to enable efficient creation of machine learning solutions).

• AKA: Automated Machine Learning Library, ML Automation Library.
• Context:
  • It can perform Model Selection through automated algorithm choice.
  • It can execute Hyperparameter Optimization via search strategys.
  • It can handle Data Preprocessing with automated cleaning and feature engineering.
  • It can support Model Evaluation through cross validation and performance metrics.
  • It can manage Model Deployment via integration interfaces.
  • ...
  • It can often reduce development time through workflow automation.
  • It can often improve model quality via systematic optimization.
  • It can often simplify machine learning for non-expert users.
  • ...
  • It can range from being a Lightweight Tool to being an Enterprise Platform, depending on its feature scope.
  • It can range from being a Specialized Solution to being a General-Purpose Framework, depending on its application domain.
  • It can range from being a Basic Automation Tool to being an Advanced ML Platform, depending on its capability level.
  • ...
  • It can integrate with Cloud Platforms for scalable computing.
  • It can support MLOps Pipelines for production deployment.
  • It can connect to Data Sources for automated ingestion.
  • ...
• Examples:
  • Open Source Solutions, such as:
    • Distributed Platforms, such as:
      • H2O AutoML for large-scale processing.
      • FLAML for resource-efficient automation.
    • Python Frameworks, such as:
      • PyCaret for low-code development.
      • Auto-Sklearn for scikit-learn automation.
  • Commercial Platforms, such as:
    • Enterprise Solutions, such as:
      • DataRobot for enterprise-scale deployment.
      • Dataiku for collaborative development.
    • Cloud Services, such as:
      • Google Cloud AutoML for cloud-native development.
      • Azure AutoML for Microsoft ecosystem integration.
  • Specialized Tools, such as:
    • Domain-Specific Solutions, such as:
      • TransmogrifAI for feature engineering automation.
      • AutoKeras for deep learning automation.
  • ...
• Counter-Examples:
  • Manual ML Frameworks, which lack automated optimization.
  • Basic Statistical Librarys, which lack machine learning automation.
  • Model-Specific Librarys, which lack comprehensive workflow automation.
• See: AutoML, Machine Learning Pipeline, Model Development Framework, Automated Optimization, MLOps Platform.

---

---

References

Let me transform this Perplexity output into GM-RKB reference format.

2024-01-02

• Perplexity.ai
  • Question: What are the most notable AutoML libraries and their key characteristics? Please describe their features, specializations, and selection criteria.
  • Answer: The most notable AutoML libraries demonstrate diverse characteristics and capabilities, ranging from distributed platforms to cloud-based solutions and lightweight libraries.
    • Popular Solutions:
      • H2O AutoML:
        • Distributed in-memory platform excelling at large dataset handling.
        • Provides comprehensive model automation for building, tuning, and deployment.
        • Supports various algorithms including gradient boosting, random forests, and deep learning.
      • Google Cloud AutoML:
        • Cloud-based solution offering automated data preparation.
        • Provides feature engineering and model selection.
        • Excels in image classification, text analysis, and tabular prediction.
      • FLAML:
        • Fast and lightweight library using BlendSearch for hyperparameter optimization.
        • Delivers high-quality models while minimizing computational resources.
        • Supports classification, regression, and time-series forecasting tasks.
    • Specialized Tools:
      • TransmogrifAI:
        • Advanced library focusing on sophisticated data preprocessing.
        • Implements meta-learning approaches for feature engineering.
        • Provides comprehensive ML pipeline automation with strength in feature selection and transformation.
      • PyCaret:
        • Low-code library simplifying end-to-end machine learning workflow.
        • Enables rapid model creation and evaluation.
        • Useful for both beginners and experienced practitioners.
    • Enterprise Solutions:
      • DataRobot:
        • Offers automated model building and deployment.
        • Provides feature engineering capabilities.
        • Includes model validation and enterprise-scale capabilities.
      • Dataiku:
        • Comprehensive platform managing end-to-end data science lifecycle.
        • Features collaborative workspace capabilities.
        • Provides extensive data preparation tools.
    • Selection Criteria:
      • For Large Datasets: H2O.ai, TransmogrifAI.
      • For Beginner-Friendly: Ludwig, AutoKeras.
      • For Model Variety: AutoGluon, Google Cloud AutoML.
      • For Customization: MLBox, TransmogrifAI.
  • Citations:

[1] https://www.geeksforgeeks.org/top-automl-python-libraries/
[2] https://www.techtarget.com/searchenterpriseai/tip/Compare-top-AutoML-tools-for-machine-learning-workflows
[3] https://www.scaler.com/blog/python-libraries-for-data-science/
[4] https://www.reddit.com/r/MachineLearning/comments/p5b6ay/r_flaml_fast_and_lightweight_automl_library/
[5] https://www.run.ai/guides/automl/automl-python
[6] https://roundtable.datascience.salon/comparing-automl-frameworks-a-comprehensive-review
[7] https://techvify-software.com/best-automl-tools/
[8] https://www.microsoft.com/en-us/research/articles/flaml-a-fast-and-lightweight-automl-library/
[9] https://openml.github.io/automlbenchmark/frameworks.html

---