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Hi HN, MLJAR is an automated machine learning platform. With mljar, you can train great machine learning models without coding. It works with binary classifica
by pplonski86 8y ago
Hi HN,
MLJAR is an automated machine learning platform. With mljar, you can train great machine learning models without coding. It works with binary classification and regression tasks. It can do hyperparameters tuning and model selection. The preprocessing to deal with missing values and categorical columns is available. All models trained in the service are by default deployed in the cloud and can be accessed be REST API or python or R APIs. User can also download a model and use it locally.
Right now it is offered as a SaaS. I'm working on the open source version. The AutoML engine is already open source https://github.com/mljar/mljar-supervised https://github.com/mljar/mljar-supervised
I've compared mljar performance on binary classification tasks with auto-sklearn and H2O and it works very well https://github.com/mljar/automl_comparison https://github.com/mljar/automl_comparison
In the long term, I would like to connect machine learning with databases. User will be able to train machine learning models by writing a SQL query to database. After the best model is trained (with AutoML of course!), all new rows that will appear in the database will be used for computing predictions. But first I would like to create AutoML platform :)
- avin_regmi 8y agoThis looks really good. I would like to speak with you. Do you have an email I can contact you?
- pplonski86 8y agoThanks! Sure, piotr at mljar.com :)
- mfatica 8y agowhat gives you the right to claim this is "The new standard in Machine Learning"?
- pplonski86 8y agoI think that AutoML will be a standard in ML. You don't want to run fit on a single algorithm, just call AutoML and specify how long you would like to wait for final model (you can also add constraints on model size and model prediction speed). Waht is more, I would like to have ML model always with explainer. I don't see disadvantages of using AutoML vs single model.
- mailshanx 8y agoCongratulations, I read your comments on the Google Auto ML thread a few days ago with great interest. If Auto ML has commercial value, why are you open-sourcing it? I think it would be a bad idea to open-source your solution, unless you plan on competing on services, rather than an Auto ML product.
- pplonski86 8y agoThank you! I believe AutoML has huge value and can be a new standard in ML. I would like to see the adoption of AutoML - that's why I'm open sourcing it. Otherwise, I would need a lot of money for marketing and sales to make it popular. Before open sourcing I was looking at Metabase and Redash solutions, and I was very impressed with their business model - I would like to achieve something similar. The goal is to be ramen profitable.
- alphagrep12345 8y agoWhy would people pay you if it's already opensourced? Do you envision a redhat kind of support system?
- pplonski86 8y agoI will offer hosted paid version (SaaS offer similar to redash) and I will offer paid white labeled embedded machine learning platform (similar to metabase). Plus paid support, paid feature requests, ML consulting. Does it make sense?
- mailshanx 8y agoLook at DataRobot - they are massively successful at this point, and I think the key is that they didn't release the source. Open sourcing the core solution hugely dilutes your value proposition - I hope that you will reconsider your decision.
- pplonski86 8y agoDataRobot has over 220M in total funding. They have resources for sales. Though, I will think about it.