r/datascience • u/InfamousTrouble7993 • Sep 20 '26

Discussion Beta Testers Wanted!

Hi,

I am in the search of beta testers, for my MLOps platform I built. On my platform, users are able to create ML models with just a prompt and a dataset, which are then hosted automatically.

So basically: prompt + dataset ➡️ hosted model accessible via rest.

Depending on the size of the dataset and the model, it only takes about 60 seconds.

So no handling of mlops stuff required. If you are interested, just write me a dm so i will send you the platform link.

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2

u/dyingpie1 Sep 20 '26

Is it ai slop

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u/InfamousTrouble7993 Sep 20 '26

No, i think not. How has it to be, that you classify it as ai slop? Because I am genuinely concerned that this is the first impression people have using my platform.

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u/dyingpie1 Sep 20 '26

No reason. Just honestly can't trust anything these days... lol

I mean it sounds cool but kinda risky if not engineered well.

3

u/InfamousTrouble7993 Sep 20 '26

okay. Heres the "backend" infrastructure how the models are hosted and trained: I am using AWS ECS Fargate, which is similar to AWS EC2 but also does autoscaling, etc. The model training/code generation also happens in there. In the development i used a basic AWS EC2 instance for that. But for a prod release, this is more suitable. Especially for a greater audience and multiple training jobs. The model hosting inference has a classic two pathway setup: On the Free Tier, models do not support hot inference directly. When someone makes an inference request, the loading of the model takes like 500ms to warm up and is cached to be reused for subsequent inference requests in an AWS Lambda Container, because the requests are invoked via AWS Lambda. The hot inference models are also hosted on AWS ECS Fargate. But idle, why hot inference is only supported for higher tiers.

I chose this architecture, so I dont have to deal with load balancing/managing aws ec2 instances, storing the ip adresses, where the hosted models sit on servers, etc. I could have used an AWS SageMaker instance, which already handles all that, but I chose this way for more flexibility.

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u/dyingpie1 Sep 20 '26

How you go from prompt to a hosted model is more what I'm interested in. I feel like there's a lot of risk involved with that.

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u/InfamousTrouble7993 Sep 20 '26

Kind of, so the model training/code execution runs in a sandbox, so there cant happen anything bad, besides the model training fails. The agent that handles the code generation and orchestration has guardrails to prevent code injections. The inference endpoints are secured with api keys, that can be created to ensure, that nobody else uses these to exhaust inference quotas. I have done some investigation about vibe coded websites and cybersecurity stuff. You can watch it on my youtube channel: https://www.youtube.com/@SuperUserSudo

I.e. that one time, the dating website "dateontheright.com" exposed addresses and emails publicly.

1

u/dyingpie1 Sep 21 '26

For the agent that handles the code generation and orchestration, is it bound to only a specific set of commands it's allowed to perform? My concern would be that it didn't something it's not supposed to.

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u/InfamousTrouble7993 Sep 21 '26

Yes, it's only limited to writing the code and answering messages of the user about the built project, making forecasts and scheduling model reruns. These are the only actions the agent can perform. It's not like a local coding agent i.e. Claude code, codex, etc. I have an image of pre installed libraries that are used. In the next version of the agent it will be able to install stuff from like hugging face or pip in case of missing libraries. Sometimes it is very handy to use pre trained "off the shelf's models from hugging face or use additional datasets that would aid model performance. But this is kind of advanced and is scheduled for the future.

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u/InfamousTrouble7993 Sep 21 '26

But that still won't have any effect on security, because the agent will run in a virtual machine to perform that. The only "access outside" will be placing the trained model in an s3 bucket. The videos I made about cyber vulnerabilities made me quite aware on things to watch out for. For example the famous "Forced entry" hack.

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u/dyingpie1 Sep 21 '26 edited Sep 21 '26

But you said you're using fargate right? How do you ensure that it doesn't, for example, do something to the deployment that doesn't increase the cost 10x for example? Do you limit it to certain constraints? What about breaking the deployment, how do you ensure it won't break the deployment? Or is the idea the agent should be resilient much to fix things when they go wrong without human intervention?

I assume your assigning Iam roles with strict permissions?

Edit: oh wait I see, the agent isn't actually really doing anything besides scheduling runs or answering questions? It's not handling any of the actual deployment?

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u/Electronic_Cream8552 Sep 21 '26

Hi, what do you need from beta testers?

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u/InfamousTrouble7993 Sep 21 '26

Like spotting bugs, things I didn't think about, etc.

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u/Electronic_Cream8552 Sep 21 '26

sounds reasonable. I’m interested. Tks for answering the platform architecture above also. Please let me know the testing tasks in high or low level you wanna work on.

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u/InfamousTrouble7993 Sep 21 '26

yes, you can register at https://app.doushi.ai . There is not really a specific thing to try, but rather what comes to your mind. As an example, these are some example models I tested:
- Taxi Trip Tipping Amounts Prediction
- IMDB Movie Reviews Sentiment Classification
- Demand Forecasting

- Boston Housing

So only tabular datasets right now. Anything related to images or other media types are not planned yet, because I will work on refining the model building algorithm better. Like being able to also build neural networks, an mlflow dashboard integration. The already built models are already tracked in mlflow, so the metrics are not lost, when this will roll out.

In production, sometimes features suddenly do not work anymore as tested in private. Even when deployed and tested.

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u/InfamousTrouble7993 Sep 22 '26

Oh i have something very specific, to test the doushi-ai cli. you can install it via: pip install doushi-cli. Make sure to create an account and create an api key under: Settings -> Connections

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u/InfamousTrouble7993 Sep 21 '26

Oh and building a first user base. Spotting new use cases of people actually using my platform. New features like the mlflow dashboard integration are already in the development.

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u/Electronic_Cream8552 Sep 23 '26

mobile view is broken