I wanted to start a broader practical use cases thread around AI Workstations.
A useful angle is data quality, but it also connects with setup choices. Different tools and workflows can produce very different results even when the goal is similar.
It would be interesting to compare practical examples, experiments, setups, or lessons that show where AI Workstations is genuinely useful and where it still needs improvement.
A useful angle is data quality, but it also connects with setup choices. Different tools and workflows can produce very different results even when the goal is similar.
It would be interesting to compare practical examples, experiments, setups, or lessons that show where AI Workstations is genuinely useful and where it still needs improvement.