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Hands-on AI and data courses built around the datasets and models on the hub — plus the courses from other providers that people most often recommend.
139 courses
Matching a store to a workload, and why "it depends" has a small number of actual answers.
The shape of a working pipeline — sources, landing, transformation, serving — and the decisions at each boundary.
Measuring whether a model harms a group, choosing a definition, and doing something about it.
Deciding whether to build something, buy it, or use the open-source option — on total cost rather than licence cost.
What scaling laws say, what they do not, and where the current paradigm struggles.
Turning things into numbers that carry meaning, and what you can do with them.
Where the model sits in the workflow, who checks it, and what happens to the exceptions.
From a promising demo to something you can operate: scope, evaluation, interface and rollout.
When data should move in batches, when it should stream, and the real cost of "real time".
Making a model return data your code can rely on, every time.
Watching inputs, outputs and the service — and knowing which alarm means what.
Splitting a corpus so retrieved passages are self-contained, and keeping the index current.