Deploy a deep learning keras model onto a web app using Streamlit.
We start by training a image classifier in tensorflow ans keras and then saving the model in google collab.
We then create a web application using streamlit and finally host the web server on the internet using ngrok application.
You will be able to deploy the ml web app directly from google collab.(Deep learning model deployment directly from google colab)
Link for tensorflow doc on image classification tutorial: [ Ссылка ]
Github Source Code: [ Ссылка ]
Recommended books for getting better at ML Web Apps and Flask:
Monetizing Machine Learning: Quickly Turn Python ML Ideas Into Web Applications on the Serverless Cloud: [ Ссылка ]
Flask Web Development: Developing Web Applications with Python: [ Ссылка ]
Hands-On Python Deep Learning for the Web: [ Ссылка ]
Flask web development from scratch : [ Ссылка ]
You can connect with me on linkedin: [ Ссылка ]
Image Classification With Streamlit| Deep Learning WebApp|
Теги
tensorflow image classificationimage classification in pythonstreamlit tutorialstreamlit pythonstreamlit python deploymentdeep learning web applicationdeep learning web appdeploying keras modeldeploy keras model with streamlitkeras and streamlittensorflow and streamlitkeras web applicationdeep learning model on web appimage classification web appimage classification using kerasstreamlit deploydeploy deep learning modelngrokgoogle collab
