Thanks a lot! We’ve tried to mimic the workflow of an ML engineer and built agents that can own specific functions of the workflow. Good to hear that the idea resonates with you!
Thanks for your feedback! The "export analysis" functionality is built to enable you to get detailed data insights and get a report generated from the insights. Would you prefer to see the entire chat in your export or would it be helpful for it to simply include code snippets in the same format you received right now?
Thanks a lot! On a side note: big fan of mljar here. When we were initially playing around with using agents for automating ML tasks, we had used problems from the openml's automl benchmark which you had posted about on Reddit for our initial tests
Thanks for the great feedback! We've added a `baseline_deployed` status where the agents create an initial baseline and deploy it so you have something to play around with quickly. This is why you're seeing a blank json there. Once your final model is deployed, it creates an input and output schema from the features used for the model build :)
We have a data enricher feature (still in a beta mode) which uses LLMs to generate labels for your data. For cleaning and feature engineering, we use agents that automatically handle it for you once you've connected your data and defined your ML problem.
P.S. Thanks for the feedback on the video! We'll update it to show the cleaning and labelling process :)