Web App Development using AI-Driven Workflows

Part 1: Foundations of AI in Web Development 

Module 1: 

1. Introduction to Web Application Development 

• Overview of web application architecture 

• Brief on front-end and back-end technologies (HTML, CSS, JavaScript, Python, Node.js, etc.) 

1. Introduction to Artificial Intelligence in Web Development 

• AI, Machine Learning (ML), and Deep Learning (DL) basics 

• Use cases of AI in web applications (e.g., personalization, predictive analytics) 

1. Setting Up the Development Environment 

• Installing necessary software (Python, Flask/Django, Node.js, etc.) 

• Introduction to version control with Git and GitHub 

Module 2: 
4. Basic Machine Learning Models 

• Overview of common machine learning models (classification, regression, clustering) 

• Hands-on session: Building a basic ML model in Python 

1. Integrating ML Models with Web Applications 

• Using REST APIs for ML model deployment 

• Hands-on session: Integrating a simple ML model with a Flask app 

Outcome: 
Participants will understand the fundamental principles of AI and web development, and they’ll develop a simple web application with a basic ML model. 

Part 2: Building and Training AI Models for Web Applications 

Module 1: 

1. Data Collection and Preprocessing 

• Understanding data types, sources, and formats (CSV, JSON, databases) 

• Data cleaning and preparation for training ML models 

• Hands-on session: Preparing a sample dataset 

1. Training and Evaluating Machine Learning Models 

• Splitting data into training, validation, and testing sets 

• Model training, evaluation, and improvement techniques (cross-validation, tuning) 

Module 2: 
3. Advanced Model Integration with Web Applications 

• Setting up RESTful APIs for model prediction 

• Implementing asynchronous calls for AI processing in real time 

1. Front-End Development for AI Interactivity 

• Building interactive front-end components (React, Vue.js basics) 

• Connecting front-end with the back-end for AI response display 

Outcome: 
Participants will gain hands-on experience in training, evaluating, and deploying ML models, with an understanding of integrating model predictions into the front-end interface. 

Part 3: Enhancing User Experience with AI-Powered Features 

Module 1: 

1. Personalization and Recommendation Systems 

• Types of recommendation systems (collaborative filtering, content-based, hybrid) 

• Hands-on session: Building a recommendation engine for a sample application 

1. Natural Language Processing (NLP) for Web Applications 

• Overview of NLP and its applications in web (e.g., chatbots, sentiment analysis) 

• Hands-on session: Building a simple sentiment analysis tool using NLP 

Module 2: 
3. Real-Time Analytics and Predictive Modeling 

• Understanding real-time data processing (e.g., user behavior tracking) 

• Hands-on session: Implementing real-time data analytics on user interactions 

1. User-Centric AI Design 

• Ethical considerations, bias in AI models, and user privacy 

• Discussing transparency and accountability in AI applications 

Outcome: 
Participants will learn how to incorporate personalized AI features and real-time analytics into web applications, enhancing the user experience with interactive and intelligent features. 

Part 4: Deployment and Maintenance of AI-Powered Web Applications 

Module 1: 

1. Deploying AI Models with Web Applications 

• Introduction to cloud services (AWS, GCP, Azure) and model deployment 

• Hands-on session: Deploying a web application with AI model on a cloud platform 

1. Scalability and Performance Optimization 

• Optimizing AI models for production (model compression, caching strategies) 

• Techniques for scaling web applications (load balancing, database optimization) 

Module 2: 
3. Securing AI Web Applications 

• Implementing authentication and authorization 

• Security best practices for handling data and user interactions 

1. Final Project and Presentation 

• Hands-on session: Building and deploying a fully functional AI-powered web application 

• Presentation of projects and peer feedback 

Outcome: 
By the end of the day, participants will be able to deploy an AI-powered web application on a cloud platform, with knowledge of scaling, security, and maintenance practices.