How to use TensorFlow for building and deploying scalable AI-powered web applications?

Building and deploying scalable AI-powered web applications with TensorFlow can be an exciting journey. Here's a simplified step-by-step guide to help you understand the process.

1. Learn the Basics: Before jumping into TensorFlow, make sure you understand the basics of Python programming, neural networks, and machine learning concepts.

2. Install TensorFlow: Visit the TensorFlow website and follow the installation guide for your operating system. You can install TensorFlow using pip by typing pip install tensorflow in your command line.

3. Explore TensorFlow: Once installed, play around with TensorFlow's basic features. Try out some simple exercises to get familiar with its syntax and functionalities.

4. Choose Your Model: Decide on the type of model that will power your AI application. This could be a convolutional neural network for image recognition, a recurrent neural network for text processing, or any other suitable model.

5. Prepare Your Data: Gather the data needed for your model and preprocess it. This might include normalizing, scaling, or encoding your data to make it suitable for training your neural network.

6. Build the Model: Using TensorFlow, construct your model layer by layer. This involves defining the architecture with the necessary input, hidden, and output layers relevant to your application.

7. Train the Model: Feed your preprocessed data into the model and train it. This is where your model learns from the data by adjusting its weights and biases to reduce errors.

8. Evaluate and Tune: Once the model is trained, evaluate its performance with a separate dataset. Tune your model by adjusting parameters or adding layers until you're satisfied with the accuracy.

9. Save the Model: After training and evaluation, save your trained model using TensorFlow's saving functionality. This will allow you to load the model later for predictions without retraining.

10. Develop the Web Application: Create the web application using a web framework like Flask or Django. Design the user interface and the backend services that will interact with your TensorFlow model.

11. Integrate the Model: Integrate your saved TensorFlow model with your web application's backend. Ensure your app can accept user input, process the data, and use the model to generate and return predictions.

12. Test Your Application: Before going live, thoroughly test every aspect of your application. Ensure it's user-friendly, the AI component works as expected, and it's resilient to different inputs.

13. Deploy Your Web Application: Choose a cloud service provider or a hosting service to deploy your web application. Follow the provider's guidelines for deploying Python web apps, ensuring scalability and security are addressed.

14. Monitor and Scale: After deployment, monitor the application's performance, user interactions, and system health. Scale your application as necessary to handle increased traffic or data loads.

15. Gather Feedback and Iterate: As users interact with your application, gather feedback. Use these insights to further improve and refine your application and model over time.

Remember, AI development is iterative. Continue to learn from your users and evolve your application to stay ahead in the world of AI-powered technologies.