Persistence App Design using AI

Duration : 48 hours

Persistence App Design Using AI

This course is designed to teach students how to design intelligent applications that remember, learn, and improve over time by combining AI technologies with persistent data storage.

In this 48-hour hands-on program, students will learn how modern applications store data locally and in the cloud, integrate AI models for smart decision-making, and design scalable app architectures. The course focuses on real-world use cases, practical projects, and best practices used in today’s AI-powered applications.

Students will explore how AI processes user data, how that data is stored securely, and how applications can retrieve and reuse information to create personalized user experiences. By the end of the course, learners will be able to design and build complete AI-driven apps that persist data across sessions and devices.

Instructor

Eng. Md Redwanur Rahman

M.Sc in Computing System Engineering, Polimi, Milano.

What You Will Learn

Fundamentals of data persistence (local, cloud, and hybrid storage)
Designing app architecture for AI-powered systems
Integrating AI APIs and pre-trained models
Managing and storing AI-generated data
Creating user interfaces that reflect stored intelligence
Ensuring data security, privacy, and ethical AI usage
Building and presenting a complete AI + persistence capstone project

Who This Course Is For

Beginner to intermediate app developers
Students interested in AI and smart application design
No-code / low-code developers (AppSheet, Firebase users)
Software developers wanting to add AI persistence to their apps

Course Outcome

By the end of this course, students will have designed, built, and presented an AI-powered application that uses persistent data storage, demonstrating real-world skills applicable to modern app development.

Course Schedule

Introduction to Persistence and AI in Apps

What is data persistence?
Types of data storage: local, cloud, and hybrid
What is AI in apps? (examples: ChatGPT, Gemini etc.)
Real-world use cases combining AI + Persistence

App Architecture for AI + Persistence

3-layer architecture (UI / Logic / Data)
AI layer integration overview
Persistence layer and database connections
Designing app workflows (data → AI → storage → UI)

Local Data Persistence

Local storage methods:
- SQLite / Room (Android)
- LocalStorage / IndexedDB (Web)
CRUD operations
Serializing AI-generated data

Cloud-Based Persistence

Firebase / Supabase basics
Connecting apps to cloud databases
Syncing local and cloud data
API authentication & user management

Integrating AI Models

Using pre-trained AI models (Text / Image / Recommendation)
Connecting AI APIs (e.g., OpenAI, Hugging Face)
Processing stored data with AI
Handling model responses and persistence

Designing Smart UI/UX

Building forms for data input/output
Showing stored history (AI results, user activity)
Providing AI feedback in UI
Designing for privacy, loading states, and offline alerts

User feedback and testing simulation

Security, Ethics, and Data Protection

Storing user data securely (hashing, encryption)
GDPR and user consent
AI bias and responsible design
Data retention and deletion policies
Ethical dilemmas

Add encryption on user consent feature

Projects

Remember user actions
Note Saver
Offline Journal
AI Feedback Collector
AI Summarizer
Secure login
Smart Shopping List
Language Learning Tracker
AI Recipe Manager
Task Management & Billing

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