Introduction
Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
Traditional machine learning model development is resource-intensive and requires a significant time commitment to produce and compare dozens of models. With automated machine learning, you can accelerate the time it takes to get production-ready ML models with great ease and efficiency.
Python has a growing ecosystem of open-source AutoML libraries. This article lists down some of the really popular AutoML libraries in Python in no particular order.
- PyCaret
- H2O AutoML
- TPOT
- Auto-sklearn
- FLAML
- EvalML
- AutoKeras
- Auto-ViML
- AutoGluon
- MLBox
PyCaret
PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine…
