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Building deployment ready containers for DL shouldn't be this hard I'm building @TensorFlowE2E in public This will help you get a Tensorflow Docker Container; ready to predict via HTTP - minus the hassle
# Day 3: Ah, leaving that logic as-is; not scraping/deleting it. Moving on to setting up train/test split for @TensorflowE2E
# Day 3: Would prefer keeping @TensorFlowE2E as much NoCode and AIO as possible; but from the looks of it; I might have to drop the file upload logic and make it a manual step. Could maybe ask for a folder with nested classes as STDIN?🤔 #thinkingOutLoud
# Day 3: Streamlit UI functional for @TensorFlowE2E. Uploads and stores data in a workspace directory as expected; but tends to freeze for a large set of images (which could be a problem down the lane).
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