How to Build a Low Pipeline for Machine Learning Workflow?

Daniel on Twitter - https://twitter.com/daslu_ Daniel on GitHub - https://github.com/daslu SciCloj - https://scicloj.github.io Study Group - https://scicloj.github.io/docs/community/groups/ processing tables: https://github.com/techascent/tech.ml.dataset github.com/scicloj/tablecloth processing arrays: https://github.com/cnuernber/dtype-next (should have mentioned) https://neanderthal.uncomplicate.org/ processing nested, unstructured data: https://github.com/clojure/core.match https://github.com/noprompt/meander https://github.com/redplanetlabs/specter math and stats: https://github.com/generateme/fastmath https://github.com/MastodonC/kixi.stats data viualization libraries: https://vega.github.io/ https://github.com/generateme/cljplot https://github.com/jsa-aerial/hanami classical machine learning: https://haifengl.github.io/ (wrapped in scicloj.ml) deep learning https://github.com/scicloj/clj-djl (wrapped in scicloj.ml) https://github.com/uncomplicate/deep-diamond machine learning workflows: https://github.com/scicloj/metamorph.ml the underlying pipeline notion: https://github.com/scicloj/metamorph wrapping all most of those machine learning libraries and workflow together: https://github.com/scicloj/scicloj.ml processing tables with spark: https://github.com/zero-one-group/geni should have mentioned: https://github.com/clj-python/libpython-clj https://github.com/scicloj/clojisr Video Courses: https://clojure.stream https://www.learnpedestal.com/ https://www.learndatomic.com/ https://www.learnreitit.com/ https://www.learnreagent.com/ https://www.learnreframe.com/ https://www.jacekschae.com/