The thing is, none of the suggested alternatives can do what pickle does, and the article focuses on a narrow (albeit ubiquitous) use case: serialisation of untrusted data.

There are still legitimate use cases for pickle, especially when storing, caching, or comparing objects that can't easily be serialised with say, JSON or TOML. It's a question of using the right thing for the right job is all, and pretending like JSON is a comparable alternative to pickle doesn't help anyone.

If you need to pickle your ML model, just use JobLib instead.

If you want to save a polars or pandas df, save files as parquet.

Both ways you can also use compression, so you'll save space as well. Use zstd if you need decent compression, or lz4 if you write and read speeds.