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Comment by nitsky | Hacker News Reader

You're right, CLI vs RStudio or IPython is not a big improvement. The real benefit here is that you can use your model from Go, JavaScript, Ruby, and other languages without having to include the Python or R ecosystems in your tech stack.

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I actually disagree, CLI is a big improvement. Vastly more developers are familiar with the command line than RStudio or IPython, including almost all of those who don’t already use some kind of ML.

Two major benefits:

* Approachability. If the first step to trying ML on a problem is “download RStudio and learn how to use a GUI and unfamiliar language” that is a HUGE barrier to entry.

* Confidence - if I’m going to put a model into production and all I have to go on is the output of a self-described novice who “played around in RStudio” a bit, well, that’s not going to happen. There are a million ways they might have messed up.

Tangram nails both of these and I’m excited to see where it goes. Happy to talk to you more (ex startup VP Product, current ML researcher) - DM me on Twitter (yieldthought).

Blindly running a CSV through a black box program is not the way to get confidence in your modeling.

I couldn’t agree more that a black box does not result in confidence in your modeling! Tangram is not a black box :) we tell you exactly the hyper parameter grid we trained, the settings that led to the best model, and provide explanations per prediction based on SHAP. And if you don’t like our settings, you can pass a config file, specifying everything from the column types in your csv, to the features produced, to the hyper parameters of the models you want trained. https://www.tangram.dev/docs/guides/train_with_custom_config...

Having a format that can be used in any language is great, and I agree this is the core problem. How does your .tangram format compare to existing attempts at standardized machine learning model formats? The only one I can think of off the top of my head is PMML and it seems like it never got much traction. Do you think tangram has a better approach to the problem?

Unfortunately, tangram does not help here, it just adds yet another format to the list. The reason it needs its own format is because .tangram files include all the model reporting information that shows up in the web application.

Another interesting direction would be to make model objects from existing popular tools (sklearn, xgboost, pytorch, etc) more portable to other languages.

We definitely will be interested in adding converters to/from other formats.

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