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Check out this excellent step-by-step demonstration of how you can develop a TF/Keras project in a Kaggle notebook and send it to Google Cloud for training at scale right from your notebook, using TF Cloud https://www.kaggle.com/rosebv/train-model-with-tensorflow-cloud …
The future is to develop/debug as fast & conveniently as possible by using the most productive tools (notebooks, Keras) then immediately start training on as much compute as you want (in the cloud) to get your results a few minutes later
@fchollet, have you tried @MetaflowOSS? Seamless interaction between local code and cloud computing through decorators - we are loving it!
That's correct.
A GPU rig is not necessary anymore.
If you have access to internet, you can use these platforms (@google):
#TeachableMachine For quick (Less than 20 minutes) prototyping
#COLAB Fast development & debugging
#GCP Access to TPUs for final TRAINING
Amazing 
!
+1, especially as performance per dollar & performance per watt continue to improve.
The question is what do you do with complicated data preprocessing issues, for instance mixing multi types data or homogenous and non homogenous time series data ? how to keep this concise ?
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