Deploy Keras Neural Network to Flask web service | Part 1 - Overview

TL;DR

This video series will cover the process of deploying a Karass model to a Flask web service, allowing other apps to access and use the model via HTTP.

Transcript

what's up guys are you ready to start a new project yeah me too so over the next several videos we'll be working to deploy a Karass model to a flask web service in this first video we're going to discuss what this means and why we'd want to do this we'll also get a glimpse of what the final product will look like so let's get to it alright so we're... Read More

Key Insights

• 😀 Deploying a model to a web service allows it to be accessed and used by other apps, regardless of their programming language.
• 👻 The Flask web service will host the Karass model and respond to HTTP requests with the model's predictions.
• 🕸️ HTML provides the structure of the web page, while JavaScript handles the logic of the web app.
• ❤️‍🩹 The video series will guide viewers through the code for both the front end and back end of the web app.
• ❤️‍🩹 Knowing Python is beneficial for understanding the back end implementation using Flask.
• 🕸️ The final product will be a simple web app that can send images to the web service and display the model's predictions.
• 🤗 Deploying a model to a web service opens up opportunities for broader usage and integration with other applications.

Questions & Answers

Q: Why would we want to deploy a Karass model to a web service?

Deploying a Karass model to a web service allows other apps, regardless of their programming language, to access the model and make predictions over HTTP.

Q: What model will be used in this video series?

The video will use the fine-tuned VGG16 model, but the steps can be applied to any model of choice.

Q: What does it mean to make an "HTTP call" to a web service?

Making an HTTP call means sending a request to the web service, asking it to perform an action. In this case, it involves requesting predictions from the model for a given image.

Q: What languages will be used for the front end and back end of the web app?

The back end will be written in Python using Flask, while the front end will be written in HTML and JavaScript.

Key Insights:

• Deploying a model to a web service allows it to be accessed and used by other apps, regardless of their programming language.
• The Flask web service will host the Karass model and respond to HTTP requests with the model's predictions.
• HTML provides the structure of the web page, while JavaScript handles the logic of the web app.
• The video series will guide viewers through the code for both the front end and back end of the web app.
• Knowing Python is beneficial for understanding the back end implementation using Flask.
• The final product will be a simple web app that can send images to the web service and display the model's predictions.
• Deploying a model to a web service opens up opportunities for broader usage and integration with other applications.
• Following along with the project will provide hands-on experience with deploying models to web services.

Summary & Key Takeaways

• This video series will guide viewers through the steps of deploying a Karass model to a Flask web service.

• The motivation behind deploying to a web service is to allow other apps, written in any language, to access and use the model over HTTP.

• The final product will be a web app that can make HTTP calls to the Flask web service and receive predictions from the model.