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?

A way to select all (or by Q/A paragraph) for export (e.g. to pdf or how about a .ipynb) would probably be the most useful. Perhaps as just a plain "export" option in addition to an analysis/insights summary.

p.s. kudos on the promo code that enable folks to kick the tires with as little friction as possible.

Makes sense! We'll be sure to make this available very soon :) Thank you!

Absolutely! Getting AI agents to determine the right set of features from raw data has been a very interesting problem

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 a lot! Excited for you to try it out and get your feedback :)

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 :)

Thank you! :)

Absolutely! We started off thinking that we wanted to automate the whole way in one go and then add restrictions and interruptions based on areas where users face issues. This is great feedback so thank you!

Hey! That sounds great. Happy to help in case you face any issues while adding support for vertex.ai.

We’ve added a tool which does EDA and when the model package is created, it contains a file called metadata.json which has detailed explanations for why a model was chosen, preprocessing steps and technical strengths & limitations. We’re working on adding an agent for performing feature engineering and should be out soon!