AutoML

Last updated Nov 14, 2024 Edit Source

Automated machine learning (AutoML) is the process of automating the process of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning.

See AI/Supervised Learning/Model selection and tuning

• https://en.wikipedia.org/wiki/Automated_machine_learning
• Automated machine learning can target various stages of the machine learning process. Steps to automate are:
  • Data preparation and ingestion (from raw data and miscellaneous formats)
    • Column type detection; e.g., boolean, discrete numerical, continuous numerical, or text
    • Column intent detection; e.g., target/label, stratification field, numerical feature, categorical text feature, or free text feature
    • Task detection; e.g., binary classification, regression, clustering, or ranking
  • Feature engineering
    • Feature selection
    • Feature extraction
    • Meta learning and transfer learning
    • Detection and handling of skewed data and/or missing values
  • Model selection. See AI/Supervised Learning/Model selection and tuning
  • Hyperparameter optimization of the learning algorithm and featurization
  • Pipeline selection under time, memory, and complexity constraints
  • Selection of evaluation metrics and validation procedures
  • Problem checking
    • Leakage detection
    • Misconfiguration detection
  • Analysis of results obtained
  • User interfaces and visualizations for automated machine learning
• AutoML conference: https://www.automl.org/
• https://www.rapidcanvas.ai/blogs/benchmarking-automl-exploring-the-top-automl-libraries
• http://www.kdnuggets.com/2017/01/current-state-automated-machine-learning.html
• https://medium.com/airbnb-engineering/automated-machine-learning-a-paradigm-shift-that-accelerates-data-scientist-productivity-airbnb-f1f8a10d61f8

• NAS is closely related to hyperparameter optimization and is a subfield of automated machine learning (AutoML).
• https://en.wikipedia.org/wiki/Neural_architecture_search
  • Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS has been used to design networks that are on par or outperform hand-designed architectures. Methods for NAS can be categorized according to the search space, search strategy and performance estimation strategy used:
  • The search space defines the type(s) of ANN that can be designed and optimized.
  • The search strategy defines the approach used to explore the search space.
  • The performance estimation strategy evaluates the performance of a possible ANN from its design (without constructing and training it).

• #BOOK AutoML: Methods, systems, challenges

• #CODE FLAML - Fast and Lightweight AutoML
  • FLAML is powered by a new, cost-effective hyperparameter optimization and learner selection method invented by Microsoft Research
• #CODE EvalML - AutoML library written in python
  • https://innovation.alteryx.com/introducing-evalml/
• #CODE Model Search
  • https://ai.googleblog.com/2021/02/introducing-model-search-open-source.html
• #CODE Auto-sklearn
  • http://automl.github.io/auto-sklearn/stable/
  • Efficient and Robust Automated Machine Learning (Feurer 2015)
  • http://www.kdnuggets.com/2016/08/winning-automl-challenge-auto-sklearn.html
• #CODE TPOT
  • http://rhiever.github.io/tpot/
  • Consider TPOT yourData Science Assistant. TPOT is a Python tool that automatically creates and optimizes ML pipelines using genetic programming
  • https://blog.alookanalytics.com/2017/05/25/automate-your-machine-learning/
• #CODE AutoKeras
• #CODE H2O autoML
• #CODE Adanet - Fast and flexible AutoML with learning guarantees
  • https://adanet.readthedocs.io
  • AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention
• #CODE FEDOT
  • Automated modeling and machine learning framework
  • https://fedot.readthedocs.io/en/latest/

• #PAPER #REVIEW An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms (Vincent 2023)

• Literature on NAS
• #PAPER AdaNet: Adaptive Structural Learning of Artificial Neural Networks (Cortes 2017)
  • https://ai.googleblog.com/2018/10/introducing-adanet-fast-and-flexible.html?m=1
• #PAPER Improving Neural Architecture Search Image Classifiers via Ensemble Learning, AdaNas (Macko 2019)
• #PAPER Up to two billion times acceleration of scientific simulations with deep neural architecture search (Kasim 2020)
• #PAPER Neural Architecture Search without Training (Mellor 2020)
  • Paper explained
• #PAPER Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines (Nikitin 2021)