vaibhavdubey97

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!

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

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!

Thank you! Making sure that basics don't going anywhere :)

Agree completely. We built Plexe with that first scenario in mind - the messy spreadsheet problem that's so common in enterprise. You can connect multiple data sources, and Plexe will identify what it needs based on the problem description. We're also gradually developing support for handling terabyte-scale data, though we're not there yet. We started by validating our approach on well-defined problems with clean datasets, but we've been systematically adding capabilities to handle increasingly complex scenarios.

On your second point about automating the "fun part", we see Plexe as amplifying that creativity rather than automating it. We're trying to make it easier to design the experiments and evaluating results. But would love to hear your feedback on this!

Thank you! Hopefully more complicated transformers are coming soon too :)

Absolutely! When we started building this out, we knew that we had to build an agent to perform data cleaning and feature transformations. After speaking to data analysts, PMs and engineers over the last few weeks, we've received strong feedback about adding this capability to Plexe and we're actively working on it. We've already added some features related to this and hopefully will roll out the whole agent very soon!

phew that was close. I'm glad your faith is restored :)

Sorry I think I explained poorly. Plexe does build deep learning models automatically. When it gets a dataset and a problem description, it automatically evaluates various model architectures (NNs being one of them).

Plexe experiments with multiple approaches - from traditional algorithms like gradient boosting to deep neural networks. It runs the training jobs and compares performance metrics across different architectures to identify which solution best fits your specific data and problem constraints.

Plexe analyzes your data and task description, then builds custom ML models using standard Python libraries (like scikit-learn, XGBoost, etc.). If your problem is best solved by a regression model, it will build that. If classification is more appropriate, it will implement that instead.

Fine-tuning existing language models is also an option in Plexe's toolkit. For example, when we needed to classify prompt injections for LLMs, Plexe determined fine-tuning RoBERTa was the best approach. But for most structured data problems (like forecasting or recommendations), Plexe typically builds lightweight models from scratch that are trained directly on your dataset.

Very cool, thanks for sharing! :)

Absolutely! And hopefully an input/output schema for the model :)

Thank you!

Smolagents works great for us but we did run into some limitations. For example, it lacks structured output enforcement, parallel execution, and in-built shared memory, which are crucial features for orchestrating a multi-layer agent hierarchy beyond simple chatbots. We've also been playing around with Pydantic AI due to its benefits with validation and type enforcement but haven't shifted yet.

You're right. We've seen the "garbage in, garbage out" problem firsthand.

We've seen the models hit typical statistical pitfalls like overfitting and data leakage during testing. We've improved by implementing strict validation protocols and guardrails around data handling. While we've fixed the agents getting stuck in recursive debugging loops, statistical validity remains an ongoing challenge. We're actively working on better detection of these issues, but ultimately, we rely on domain expertise from users for evaluating model performance.

Thank you!

Yeah we rushed to create a "Plexe in action" video for our Readme. We'll put a link to the YouTube video on the Readme so it's easier.

Using large generative models enables fast prototyping, but runs into several issues: generic LLMs have high latency and cost, and fine-tuning/distilling doesn’t address the fundamental size issue. Given these pain points, we realized the solution isn’t bigger generic models (fine-tuned or not), but rather automating the creation, deployment, and management of lightweight models built on domain-specific data. An LLM can detect if an email is malicious, but a classifier built specifically for detecting malicious emails is orders of magnitude smaller and more efficient. Plus, it's easier to retrain with more data.