Fastai

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part of my studying of deep learning.

TODO:

  • Create simple end-to-end app

Lesson 3 Production and Deployment

DataBlocks go before a dataloaders?

bear = bears.new(item_tfms=Resize(128), batch_tfms=aug_transforms(mult=2))
dls = bears.dataloaders(path)
dls.train.show_batch(max_n=8, nrows=2, unique=True)

So create a datablock, then create a dataloaders with it? Then you can train the loaded data? I geuss.

It’s much easier to clean data after you train your model. This allows you to quickly find least confident elements and remove or move them. FastAI does this with the ImageClassifierCleaner, which takes a learner as an argument.

Deployment

Trained models can be exported to a .pkl, which is basically all the information of the model. This .pkl file can later be imported, and used to predict.

You can serve your Jupyter notebooks online! you can use Voila to build it, and Binder to run it.

Deploy to mobile

Deploy to server, and serve on mobile, it’s easier.

How to deploy:

  1. Manual Process
    1. Run model in parallel with normal systems
    2. Humans check every prediction
  2. Limited scope deployment
    1. careful human supervision
    2. Time and/or geography limited
  3. Gradual Expansion
    1. Good reporting systems needed
    2. Consider what could go wrong

Beware feedback loops -> especially with models whose predictions affect their future inputs.

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