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dark_pattern

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投稿

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1 ポイント·投稿者 dark_pattern·4 年前·0 コメント

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1 ポイント·投稿者 dark_pattern·4 年前·0 コメント

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1 ポイント·投稿者 dark_pattern·4 年前·0 コメント

[untitled]

1 ポイント·投稿者 dark_pattern·4 年前·0 コメント

[untitled]

1 ポイント·投稿者 dark_pattern·4 年前·0 コメント

[untitled]

1 ポイント·投稿者 dark_pattern·4 年前·0 コメント

Picking Color via Eyedropper on Web App

canvatechblog.com
3 ポイント·投稿者 dark_pattern·4 年前·0 コメント

Going deeper with depth maps to Auto Focus

canvatechblog.com
2 ポイント·投稿者 dark_pattern·5 年前·0 コメント

Machine Learning Hyperparameter Optimization with Argo

canvatechblog.com
2 ポイント·投稿者 dark_pattern·5 年前·0 コメント

Scaling Subscription Analytics at Canva

canvatechblog.com
1 ポイント·投稿者 dark_pattern·5 年前·0 コメント

コメント

dark_pattern
·4 年前·議論
It'll depend on your needs, you have to compute your precision against recall to then decide what is a good cutoff for your application
dark_pattern
·5 年前·議論
One of the authors here.

Just to add: the MLOps ecosystem evolves very quickly.

Tools that were once favored before can become obsolete very quickly. The general philosophy is to keep a constrained and minimal toolset: if we have a general purpose tool that could be extended to other cases too, then we do that. Moreover any introduced tool has tradeoffs not always apparent: additional staff training and maintainence costs.

That being said, it'd be interesting to see how the space evolves over the next few years. I think we'll eventually see best practices arising from the space, but for now it's very nascent.