I wanted to start a broader ideas worth trying thread around Machine Learning.
A useful angle is testing, but it also connects with quality versus speed. 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.
A useful angle is testing, but it also connects with quality versus speed. 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.