Yep, we made our own. I haven't heard of PMML before - quite cool! What we've made is a bit more readable for what we're using it for though, IMO. Looks like this:

    {
        "intercept": 1.0,
    
        "features": {
            "feature_1": {
                "coefficient": 1.0,
                "range": [0.1, 10.0],
                "mean_feature_score": 1.0,
                "imputation_value": 1.0
            },
            {
                ....
            }
        }
    }

Is this open source? We were looking for something like this.

Sadly not. I'd be totally up for open sourcing if there's clear demand. If you can find it, send me an email at angus@{company_I_work_at}.com

Note that it's very tied down to our use case right now: only compatible with Logistic Regression, and currently it assumes fixed hyperparameters (will change this in future though), assumes a production pipeline of min-max scaling, imputation, then classification.

PMML is fairly verbose and limited to a particular set of models. It's often easier to pickle the models and then keep tagged versions. I think a human readable format could be created, but since most models are just a pile of numbers it's unclear what is gained.

For Logistic Regression we find human readable config makes a lot of sense. It's pretty intuitive if there aren't too many features - if the model starts behaving weirdly, we can sometimes track it down to a change in a single feature using this (especially when viewing recent git diffs).

Sure. I tend to keep my postprocessing of a model under version control. In particular, what features were most helpful for predictions.

Can't really talk about features on here :(