Colab link: https://colab.research.google.com/github/fastai/fastbook/blob/master/02_production.ipynb
Drivetrain approach
![notion image](https://www.notion.so/image/https%3A%2F%2Fs3.us-west-2.amazonaws.com%2Fsecure.notion-static.com%2F149efb40-c4d1-447e-8da1-75ece94cbe0e%2FScreenshot_from_2021-06-09_18-11-59.png%3FX-Amz-Algorithm%3DAWS4-HMAC-SHA256%26X-Amz-Content-Sha256%3DUNSIGNED-PAYLOAD%26X-Amz-Credential%3DAKIAT73L2G45EIPT3X45%252F20221016%252Fus-west-2%252Fs3%252Faws4_request%26X-Amz-Date%3D20221016T192508Z%26X-Amz-Expires%3D86400%26X-Amz-Signature%3Daf4972c41fdae8da0df1c37f0f94ea35bd5dad04bc2b0a186ddd9e24a68a91a7%26X-Amz-SignedHeaders%3Dhost%26x-id%3DGetObject?table=block&id=ae06976b-b61c-4111-b555-c41a3df2808f&cache=v2)
Production Classifier
Steps:
- Data
- Gather data
- Verify
- Create data block (variable types, path to data, transforms, etc...) & loader
- Tweak
- Create learner (loader + model + metric)
- Quick train
- Create interpreter & plot (e.g. confusion matrix, top/bottom losses)
- Clean data
- Full train
- Export & deploy (steps depend on application)