Plexe automates the full ML lifecycle from messy data to deployable models. Run 50 plus diagnostic tests, detect failure modes, and generate insights, dashboards and models using plain English. No notebooks. No guesswork. Just results.
This is the 2nd launch from Plexe. View more
Plexe
Plexe automates the full ML lifecycle from messy data to deployable models. Run 50 plus diagnostic tests, detect failure modes, and generate insights, dashboards and models using plain English. No notebooks. No guesswork. Just results.
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Launch Team / Built With
Plexe
Note: We're getting a lot of users asking about trying the platform. You can try Plexe now with promo code "LAUNCHDAY20" to get free $20 credit!
Hi Product Hunt community!
As ex-ML engineers, we’ve always seen that building ML models takes months. So we set out to fix that. With Plexe, you can build and deploy ML models 10x faster from plain English.
Plexe connects to your data sources and builds ML pipelines autonomously. Based on your problem description, it discovers your data, performs feature engineering, experiments with model architectures and deploys production-grade models. It can also visualise your data, create dashboards and help you uncover deep insights from your data.
The Problem
Lots of great use cases for ML models in businesses never materialize because ML projects are messy and convoluted. You spend months finding the data, cleaning it, experimenting with models and deploying them to production, only to find out that the project has been binned due to taking so long. At a previous company, we witnessed a team of 10 ML engineers spend 2 years and $3M building models for a project that never saw the light of day.
There are several tools for “automating” ML, but it still takes teams of ML experts to actually productionize something of value. And we can’t keep throwing LLMs at every ML problem. Why use a generic 10B parameter language model, if a logistic regression trained on your data could do the job better?
What have Plexe users shipped?
Asset price prediction: https://www.linkedin.com/posts/laxmi-prashanthi-muthyala_plexiai-mlmodel-machinelearning-activity-7375974160373661696-tw-i
Investment performance optimizer: https://console.plexe.ai/share/c9af134d-bac4-42aa-8b35-7ba49c804618
Fraud detector: https://console.plexe.ai/share/9d6665f7-11ab-470a-ad60-64011663d31b
Logistics & delay forecaster: https://console.plexe.ai/share/fe27d994-9347-44d2-80aa-4a1d901c57b7
Reddit’s pizza request success predictor: https://console.plexe.ai/share/3d37880a-c418-4f2b-a2ca-a9588c66c410
Along with individuals, companies are using Plexe to ship recommendation engines, anomaly detection, lead scoring and more!
@vaibhav_dubey3 Super cool! I really enjoyed the clean UI — it feels intuitive, efficient, and easy to navigate.
I had a few questions:
You mentioned in the performance section that you do a bunch of benchmarking. Could you elaborate on what you use as baselines for the models built by Plexe? What happens if the baseline performs better than the model (or does that generally not happen)?
I’d be really keen to have AI-generated labels. In my world, I always have the data but not the labels — for example, I might have reviews but not the sentiments. Is that something you’re planning to add?
@vaibhav_dubey3 Oh and also, what do you mean by Diagnostic Tests?
Plexe
@vaibhav_dubey3 @sonali_syngal great question - for every model Plexe builds, a set of evaluations is carried out to produce a report that helps the user understand the behaviour, performance, and limitations of the model, so they can make an informed decision about productionisation.
This includes things like feature importance, robustness to perturbations, distribution of incorrect predictions, and much more - all in comparison to a baseline heuristic selected for the problem. These metrics are all explained to the user in plain English and available in the "Evaluations" tab of the model page.
You can see an example here: https://console.plexe.ai/share/9d6665f7-11ab-470a-ad60-64011663d31b.
Which metrics do you usually find most helpful in explaining a model's behaviour to non-technical stakeholders?
@vaibhav_dubey3 @mdebernardi1 makes sense. I think the metrics you have in the examples here are what I usually use. I guess Precision Recall are the ones I look at most
I like the detailed analysis and evaluation, the overall report is really good!
Plexe
@sonali_syngal Thanks a lot for your feedback! That UI has gone through multiple iterations so I'm glad to hear that it feels intuitive :)
To answer your questions:
1. Great question! We take model validation very seriously. Before building any model, our system automatically generates baselines tailored to your specific problem based on your data characteristics and task type. For example:
For classification tasks: we use simple heuristics like majority class prediction or stratified random sampling.
For regression: we use statistical baselines like mean/median prediction or simple trend analysis.
For time series: we implement seasonal naive forecasts, moving averages, or last-value propagation.
If the baseline outperforms the model, we currently report this in the evaluation report with detailed performance comparisons, but the model still gets deployed. However, we flag it as 'CONDITIONAL_PASS' with recommendations. This gives you full transparency - you can see exactly how much lift you're getting over the baseline and make an informed decision. We're also working on adding automatic alerts when baselines win by significant margins, which would trigger a review before deployment!
Yes! We have a data generation feature that uses LLMs to generate labels for unlabeled data. You can upload your reviews, specify you want a 'sentiment' column, and our system uses LLMs to generate labels for you. The feature is in early stage right now, but we're actively ramping it up with a pipeline of specialized LLMs, quality controls, confidence scoring, and tighter integration into the model building workflow. We'd love to understand your specific labeling needs to help prioritize what we build next!
@vaibhav_dubey3 Hey! A few questions about Plexe that I wanted to clarify:
As a PM, I wanted to understand how does Plexe ensure that non-ML-experts can still build good models, what guardrails, automation or guidance are in place?
When I productionize my model, how does Plexe handle scaling, basically I’ve built a model and want to support high throughput or many users, what happens?
And last question, how does Plexe manage data privacy and security, especially if I bring my own data or deploy in a customer-facing environment?
Plexe
@bianca_riat1 Great questions!
We've built a rigorous evaluation suite that runs 50+ tests on every model so you get all the details about the model's performance before you start using it. Here's a sample of the evaluations that get run: https://console.plexe.ai/share/5e996b22-1cbd-4596-bc11-30b88f8d07ce
We provide a serverless inference endpoint that automatically handles scaling up and down based on your traffic. And you only pay for the requests you make to the model!
The entire Plexe platform is self-hostable for enterprises. In our public version, we follow a multi-tenant architecture that ensures that a user's data is not shared with anyone else
What type of models do you think you'd like to build with Plexe? :)
@vaibhav_dubey3 very interesting.
Could you give me ideas on how i can use plexe for my online store. I sell a bunch of stationery, gifting items, craft items etc physically and online.
Plexe
@nidhi_maurya1 Hey! We can help you get insights about what your customers are buying and are likely to buy. You can also use Plexe to get forecasts for what products are likely to be purchased in the coming weeks so you can use data for inventory management :)
We'd love for you to try the product and let us know if it turns out to be useful for these use cases!
@vaibhav_dubey3 Plexe building and deploying models straight from plain English is unique. Excited to give it a spin with that launch code! :D
Plexe
@vaibhav_dubey3 @rohanrecommends
Wonderful! Looking forward to hearing about what you try out!
Plexe
@rohanrecommends This is very exciting to hear! I'm interested to learn more about the models you build so please reach out once you've given it a spin :)
@vaibhav_dubey3 So true. Too many ML projects die in experimentation, not every problem needs an LLM. 😅
Plexe
@vaibhav_dubey3 @himani_sah1 Yes ,rightly said! Do you want try it out? We'd love to hear your feedback!
Plexe
@himani_sah1 I know right! We were tired of seeing the solution being "let's just an LLM at this" instead of creating a tiny, lightweight model that performs better than generic LLMs :)
Plexe
Why do we need an AI data scientist?
In our combined ~20 years of ML work, we’ve seen the same pattern repeat: in every project, data scientists spend roughly 20% of their time understanding the business context and objectives, 60% manipulating data, and 20% building models. More often than not, that first 20% has by far the greatest impact on the project's success, while the remaining 80% is mostly "undifferentiated heavy lifting": repetitive and formulaic (yet highly technical) work that follows the same patterns across projects.
That 80% has two major consequences:
Data scientists spend most of their time on repetitive technical work instead of deeply understanding the broader context.
The technical know-how prerequisites of that work locks out others (software engineers, analysts, PMs).
If we automate the technical but formulaic 80%, we can make data scientists far more productive, as well as open up ML to everyone else.
What would you build if technical know-how wasn’t a barrier?
@mdebernardi1
Hi all! This makes a lot of sense, but if you automate that 80%, how do you make sure a user or a business can still understand what the model is doing instead of becoming even more of a black box?
Plexe
@saradb hey, great question, and this is indeed a tricky problem! We do several things to make the platform's decisions and the final models as transparent to the user as possible:
The full chain of reasoning of the data scientist agents is available to the user for auditing.
A comprehensive model evaluation is produced for every model so the user can understand the model's behaviour, its limitations, and how these relate to the data it was trained on (feature importance, data quality issues in the training data, etc).
The entire model bundle is itself available to users, allowing full auditing of the model "deliverables" themselves.
In other words, out platform does all of the things that a data scientist would do in order to ensure their work is not a black box!
Product Hunt Wrapped 2025
Super cool. Turning messy data into deployable models with plain English is a dream. The 50 plus tests and failure mode checks feel production ready. How do you handle data governance and model drift over time? Also, any git or CI hooks for teams?
Plexe
@alexcloudstar these are great questions!
While this is an early release, we're conscious of how critical these governance aspects are to ML models actually landing in production. Right now, we support dataset versioning plus periodic model retraining and re-evaluation to cover the basics. In future releases, we plan to add mechanisms such as data lineage, granular data access controls for multi-user accounts, and automated continuous drift monitoring for deployed models. The exact roadmap will be determined by our users' most pressing needs.
Regarding git or CI hooks: not out yet, but it's already been requested and is in the works! ;)
Nas.io
Love how Plexe cuts out the ML busywork, feels like the future of data science, but actually usable.
Plexe
@nuseir_yassin1 Thanks for the feedback! Can't wait to see what you build! You can also share your model to showcase your amazing work.
Plexe
@nuseir_yassin1 Your comment made our day! I'm a huge fan of your YouTube channel! It would be incredible to have you try our product!
Plexe
@nuseir_yassin1 thanks a lot, and yes, we completely agree with your take! We believe this is going to open up data science and its benefits to a lot more people.
We'd love for you to stress-test the platform and tell us your thoughts :)
Plexe
@nuseir_yassin1 Thanks a lot!
Used Plexe for my final year project, and it's been a huge time saver!
Got early access to Plexe for my university project and honestly, it made the entire ML workflow so much easier. I didn’t have a big dataset or months to train models, but Plexe handled data prep, feature engineering and even deployment on its own. The model I built for my project was live in hours, instead of weeks and the insights dashboard made my presentation look super polished.
If you’re a student or an engineer looking to get real ML results fast (without fighting with ML pipelines all night 😅), definitely give it a try.
Plexe
@angad_riat That sounds amazing. This is what we're solving for exactly at Plexe, production grade models in hours instead of weeks! Thanks for the feedback! You can also share your model to showcase your amazing work! Will be looking forward to seeing what you were able to build with Plexe
Plexe
@angad_riat thanks for your feedback and for trying us out! In which parts of the project (data prep, deployment, ...) do you think Plexe was most valuable to you personally?
Plexe
@angad_riat That sounds fantastic! Thanks a lot for sharing your experience with Plexe. Would love to hear more about the model you built :)
@vaibhav_dubey3 Thanks! I used Plexe to build an ML based price prediction model for used cars in the Indian market. My dataset was quite primitive, but Plexe’s automated feature engineering and fine tuning delivered significantly better regression metrics than anything I could get manually. It ended up giving me the best working model for my project, all within a few hours!
Plexe
@angad_riat That’s awesome! 🚀Thanks for sharing.
Lancepilot
Congrats on the launch. Plexe sounds like a massive step forward for simplifying the ML workflow building and deploying models with natural language is such a powerful concept. Excited to see how it empowers data teams to move faster with less friction.
Plexe
@priyankamandal thank you! We're excited about all the people who will gain access to "ML building" now that Plexe can abstract a lot of the technicals for them. We believe in a future where anyone can fully leverage their data :)
What use cases do you think are the most exciting for data teams in your field?
Plexe
@priyankamandal Thanks for the feedback! Can't wait to see what you build!
Plexe
@priyankamandal Thanks so much for the kind words! 🙏 We're really excited about this direction. Since you mentioned Data teams - we have integrated connectors for literally every data source, from spreadsheets and warehouses to API's and vector DBs, perfect for Data teams for building analytics dashboards as well.
Great launch. I have a few questions:
Can it handle any type of ML use-case? For example, image classification, text summarization etc
How do we ensure data privacy and security while using a tool like this?
Do you have any metrics on fairness/ bias of the models
Plexe
@yash_pal_paul_syngal great questions, thank you for asking!
We currently support structured, relational data, which covers all sorts of use cases from fintech to ecommerce. Support for image, audio and video data coming soon!
Our platform fully segregates different users' data at the infrastructure level, ensuring that even in the event of an application failure, data remains only accessible to each user. For businesses with more stringent privacy/compliance requirements, we offer the option to host our platform in their own air-gapped environment, with deployment support from us.
Yes, this is part of the model evaluation tests we perform! We compare model predictions by different data subgroups to flag fairness issues. You can see an example in this screenshot, where the evaluation highlights different prediction biases by demographic. In addition, we compute 50+ other metrics, you can check out examples in the "evaluations" tab of the model page.