Tools for the machine learning workflow: labelling training data, building and training models, tracking experiments and running open-weight language models locally, for researchers and ML teams.
Training a model of your own is a pipeline, and this category is easiest to read as one. Data has to be labelled and checked, a model built and trained, experiments logged so the good run can be reproduced, and the result run somewhere useful. The tools here cover each stage, and few teams need one platform that does all four.
Labelling tools differ most in quality control, so ask how disagreements between annotators are caught. For experiment trackers, check that a past run can be reproduced from what was logged, code version included. If your data is Arabic, evaluate on real Arabic text from your own domain early, including the dialects your users write in.
To build on an existing model rather than train one, see AI Data.
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