Deploying a Simple TensorFlow Model | Challenge

# Introduction

This project is designed to guide you through the process of creating a simple TensorFlow model, exporting it, and then serving it using Docker and TensorFlow Serving. TensorFlow is an open-source machine learning framework, and TensorFlow Serving is a flexible, high-performance serving system for machine learning models. Docker containers make it easy to package and deploy these models consistently. By the end of this project, you'll understand how to set up a basic machine learning model in TensorFlow, export it for serving, and deploy it using TensorFlow Serving inside a Docker container.

## 👀 Preview

# Send a prediction request to the TensorFlow Serving container
curl -X POST \
  http://localhost:9501/v1/models/half_plus_two:predict \
  -d '{"signature_name":"serving_default","instances":[[1.0], [2.0], [5.0]]}'

Output:

{
  "predictions": [[2.5], [3.0], [4.5]
  ]
}

## 🎯 Tasks

In this project, you will learn to:

- Install TensorFlow and TensorFlow Serving dependencies
- Create a simple TensorFlow model for basic arithmetic operations
- Export the model in a format suitable for serving with TensorFlow Serving
- Serve the model using Docker and TensorFlow Serving
- Send prediction requests to the deployed model and receive predictions

## 🏆 Achievements

In this project, you will learn:

- How to install TensorFlow and TensorFlow Serving dependencies
- How to define and export a simple TensorFlow model for serving
- How to serve a TensorFlow model using Docker and TensorFlow Serving
- How to send prediction requests to the deployed model and observe predictions