MLArtisan

I’m working on MLArtisan, a platform that helps with integrating machine learning into real products without building ML infrastructure from scratch.

Right now, I’m looking for real problems to solve, not just to promote a tool.

If you’re working on a project and thinking:

• "ML could help here, but I’m not sure how."

• "This feature feels too complex to build without ML."

• "We don’t want to build training pipelines and prediction services ourselves."

Some examples where ML might help:

• price or demand prediction

• recommendations (content, products, actions)

• user segmentation or clustering

• scoring, ranking, or classification

• anomaly or trend detection

Bring your idea here.

We can discuss your use case, data shape, and constraints, and see whether it can be solved using MLArtisan.

You can reply in the comments or ask questions directly.

I’m actively using feedback to shape the product.

I’m Alexey - a full-stack engineer who loves building tools that remove unnecessary complexity.

Today I’d like to share what I’ve been working on: MLArtisan.

The Pain That Started Everything

A few years ago, our small team tried to integrate ML into a product.
Not “deep research ML”, just practical things like predictions, clustering, or simple models per customer.

But here’s what actually happened:

• Endless library research (many were Python-only, but we were on C#)

• Dataset management became a project of its own

• Experimentation was slow, disorganised, and hard to compare

• The infrastructure setup took more time than the model itself

• Deployment required writing custom services for each model type

• Updating a model often meant updating the codebase

• Customer-specific models added even more complexity

We spent more time on infrastructure than on machine learning.

At some point, I realised:

“To create an ML model, teams really need just four steps:
Schema > Data > Train > Predict.
Why is everything else so complicated?”

That became the seed of MLArtisan.

What MLArtisan Does

MLArtisan is a lightweight ML platform designed for developers, small teams, and indie builders who want ML without heavy infrastructure.

1. Define input/output schemas: This structures your data and lets you keep consistent versions.

2. Upload datasets: Version, filter, and prepare training data with minimal friction.

3. Train models for your case: Neural Networks, RNNs, K-Means, SVM, Naive Bayes, Logistic Regression, ONNX, and more. Each of them supports different types of Hyperparameters like learning rate, layers, batch size, clusters, etc. If your machine has a GPU, the app will use it automatically (self-hosted version).

4. Deploy instantly: Every model gets a prediction API. No pipelines, no extra services, no infrastructure setup.

There is a simple model registry:

• Each model has versions

• Last metrics stored

• Copy models (e.g., "experiment" > "stable")

• Improve one while another stays in production

• More features, like tags and metric comparison, are planned.

Cloud or self-hosted: There’s a Docker image for teams who want full control: https://hub.docker.com/r/alexeymlartisan/mlartisan-selfhosted

Who It’s For

• SaaS founders adding ML predictions

• Indie hackers building data-driven features

• Internal tools needing simple ML

• Automation teams

• People who want ML without learning TensorFlow/PyTorch

• Anyone who wants to run it in the cloud OR entirely self-hosted

Basically, anyone who wants practical ML without rebuilding the same infrastructure from scratch.

Current State: Early Open Beta

The project is fully functional but still early.
You may find bugs, rough edges, or be slow in some places - and that’s totally expected.

You can try it here:

• Cloud: https://mlartisan.com

• Self-hosted: https://hub.docker.com/r/alexeymlartisan/mlartisan-selfhosted

• Bugs/feedback: https://github.com/orgs/MLArtisan/discussions/categories/issues-bug-reports

• Q&A: https://github.com/orgs/MLArtisan/discussions/categories/q-a

• Ideas: https://github.com/orgs/MLArtisan/discussions/categories/ideas

I’m personally answering everything during the beta.

What I’m Looking For

• Early testers

• Honest feedback

• Feature requests

• Real-world use cases

• Bug reports

• People who want to integrate ML and tell me where things break

If you're building something and want to test MLArtisan, I’d love to hear from you.
You can comment here, DM me, or reach me at: contact@mlartisan.com

Thanks for reading!
I excited to get your thoughts!