It's seems like a new competing approach in the ML space. The standard julia data pipeline right now usually involves either directly using an independent packa

It's seems like a new competing approach in the ML space. The standard julia data pipeline right now usually involves either directly using an independent package for each step (or rolling your own since Julia has strong native support for fast data/number manipulation) or using a package that combines all those functionalities like MLJ.jl [1] for ML and Queryverse.jl [2] for data manipulation and visualization. The advantage is that since Julia lang focuses on composability, those frameworks can easily share core functionality so library creators don't need to reinvent the wheel and all frameworks can benefit from each other works. [1] https://github.com/alan-turing-institute/MLJ.jl https://github.com/alan-turing-institute/MLJ.jl [2] https://www.queryverse.org/ https://www.queryverse.org/