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AI Based Application Development

How to start building AI Based Applications that make your client's live easier, with sample practical case studies. Utilize existing LLM model, eg OpenAI, deepseek, etc. Using python or node js as the client application library

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Training Objectives

This training aims to equip participants with the knowledge and skills to initiate the development of AI-based applications that enhance client experiences by simplifying daily tasks. It focuses on leveraging existing Large Language Models (LLMs) such as OpenAI and DeepSeek, using Python or Node.js for client-side integration, and includes hands-on case studies to demonstrate practical implementation.

Target Participants

This program is designed for software developers, data scientists, product managers, and IT professionals with basic programming experience in Python or JavaScript, who are interested in integrating AI capabilities into applications to solve real-world problems for clients.

Standard Modules to Be Covered

  • Introduction to AI-Based Application Development
  • Overview of Large Language Models (LLMs) and Their Capabilities
  • Selecting and Accessing LLMs: OpenAI, DeepSeek, and Alternatives
  • Setting Up Development Environments for Python and Node.js
  • Integrating LLMs into Client Applications Using APIs
  • Designing User-Centric AI Solutions for Client Needs
  • Practical Case Studies: Real-World Application Scenarios
  • Best Practices for Security, Scalability, and Ethical AI Use
  • Testing, Debugging, and Deploying AI Applications

Additional Materials

Participants will receive access to code repositories with sample projects, API documentation for LLMs, cheat sheets for Python and Node.js integration, video tutorials, and a curated list of resources for further learning and development.

Training Methods

The training employs a blend of interactive lectures, live coding demonstrations, hands-on labs, group discussions, and collaborative project work. Case studies will be used to contextualize learning, and participants will engage in building mini-projects to reinforce concepts.

Expected Outcomes

Upon completion, participants will be able to design, develop, and deploy AI-based applications that leverage LLMs to address client challenges. They will gain proficiency in using Python or Node.js for integration, apply best practices in AI development, and create functional prototypes demonstrated through case studies.

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What you will learn?

Understanding AI and Its Business Value

  • 1. What is Artificial Intelligence and Why It Matters

  • 2. Types of AI Applications That Transform Businesses

  • 3. Real-World Problems Solved by AI

  • 4. Measuring ROI for AI Implementations

  • 5. Ethical Considerations in AI Development

Choosing the Right AI Foundation

  • 1. Overview of Large Language Models (LLMs)

  • 2. Comparing OpenAI, DeepSeek, and Other Providers

  • 3. Factors to Consider: Cost, Capabilities, and Compliance

  • 4. Setting Up Developer Accounts and API Keys

  • 5. Understanding Rate Limits and Best Practices

Building Your Development Environment

  • 1. Python vs Node.js: Choosing Your Stack

  • 2. Setting Up Development Environment

  • 3. Essential Libraries and Dependencies

  • 4. Version Control and Project Structure

  • 5. Testing and Debugging Tools

Core AI Integration Patterns

  • 1. REST API Fundamentals with AI Services

  • 2. Synchronous vs Asynchronous Communication

  • 3. Handling API Responses and Errors

  • 4. Implementing Retry Logic and Fallbacks

  • 5. Security Best Practices for API Keys

Practical Case Study: Customer Service Chatbot

  • 1. Defining the Business Problem

  • 2. Designing Conversation Flows

  • 3. Implementing with OpenAI API

  • 4. Adding Context and Memory

  • 5. Deployment and Monitoring

Practical Case Study: Document Analysis System

  • 1. Automated Document Processing Use Cases

  • 2. Text Extraction and Analysis with DeepSeek

  • 3. Building Classification and Summarization

  • 4. Handling Multiple File Formats

  • 5. Integration with Existing Workflows

Practical Case Study: Personalized Recommendation Engine

  • 1. Understanding User Behavior Patterns

  • 2. Building Recommendation Algorithms

  • 3. Implementing with Multiple AI Models

  • 4. A/B Testing and Optimization

  • 5. Scaling for Large User Bases

Advanced AI Application Features

  • 1. Implementing Multi-Model Architectures

  • 2. Building Custom Training Pipelines

  • 3. Real-time Data Processing

  • 4. Caching and Performance Optimization

  • 5. Handling Edge Cases and Ambiguities

Deployment and Scaling Strategies

  • 1. Containerization with Docker

  • 2. Cloud Deployment Options

  • 3. Load Balancing and Auto-scaling

  • 4. Monitoring and Logging

  • 5. Cost Management and Optimization

Maintaining and Improving AI Applications

  • 1. Continuous Integration and Deployment

  • 2. Model Versioning and Updates

  • 3. Gathering User Feedback

  • 4. Performance Monitoring and Analytics

  • 5. Planning for Future Enhancements

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