u/Aleksandra_P — reddlx

I want to share an interesting overview about AutoML open-source trends. It’s no longer ofonly about which framework gives the best score?

One thing that surprised me while researching this is how different the goals of modern AutoML tools have become. Some frameworks optimize for benchmark performance.
Some focus on explainability and reproducibility. Some are becoming full AI-powered ML engineering systems.

In this article you can find:

which projects are still actively maintained,
which older frameworks are slowly becoming legacy tools,
GPU vs CPU-oriented approaches,
local-first vs cloud-first workflows,
and how agentic ML systems are changing the ecosystem.