I wanted to start a broader creative experiments thread around Machine Learning.
A useful angle is troubleshooting, but it also connects with performance. 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 Machine Learning is genuinely useful and where it still needs improvement.
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A useful angle is troubleshooting, but it also connects with performance. 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 Machine Learning is genuinely useful and where it still needs improvement.
Photo by on
