Model composability is one of Julia's and MLJ's highlights. Library designers tend to take this into account throughout the ecosystem. It helps that Julia does not need to dropdown into a C or FORTRAN wrapper like Numpy.<p>That being said, MLJ tends to wrap a lot of models from scikit-learn with a composable wrapper, meaning you end up having to manage Python dependencies and you lose Julia features such as multi-threading (Edit: when using Python models, multi-threading is supported when using Julia models).<p>Long term I am sure MLJ will slowly replace scikit models but right now it can be little painful to use if the model you want is not implemented in Julia.
There are some very cool features with MLJ. You can query models() and find all of the machine learning models that your data works with. This is great for easily plugging into various models to see what works.<p>The problem I ran into is that it can be very unclear when things don't plugin exactly as planned. You get a typing error that looks like C++ template errors. Or your data doesn't work quite right with a model and needs some transforming but the documentation doesn't spell how to do with your situation. There aren't nearly as many StackOverflow questions on using Julia as there are Python ones so you can't just look up a very specific question and get answers.
For those with experience with ML in Julia I’d love to get some advice. I have a little business that sells 3D printed jewelry (<a href="https://lulimjewelry.com" rel="nofollow">https://lulimjewelry.com</a>). My biggest seller is customers engraving their or their loved ones fingerprint on the ring. Most of those prints come in needing manual cleanup, which I can usually do in a few minutes.<p>I’d love to train a ML algorithm to do this, and I’ve been building up the before and after pictures over time using my manually cleaned up customer fingerprint images.
Can anyone give me suggestions or pointers on the sort of algorithm that may be best suited for this task? Just something to get me started down the correct path would be very helpful.
I do not understand why someone should use this? It is basically a wrapper of existing Libs and Frameworks. This framework does not solve any problem that scikitlearn and other existing frameworks have not already solved.
This looks very similar to<p><a href="https://github.com/IBM/AutoMLPipeline.jl" rel="nofollow">https://github.com/IBM/AutoMLPipeline.jl</a>
I want to love MLJ and I _do_ love Julia, but holy wow is it hard to learn. I think three times now I've had a small dataset I wanted to do something simple like linear regression on, thought it'd be a good opportunity to learn MLJ, and ended up giving up when I was knee deep in inscrutable errors about scientific types and machines and unsupported models.<p>If anyone has a good introduction to recommend which is clearer than the official docs, I'd definitely appreciate it.