JSLeague

Course duration

2 days

Technical requirements

80%
Programming experience
70%
Web APIs and JSON
70%
Familiarity with Git

Course Overview

This intensive 2-day program equips technical professionals with practical skills to build AI-powered prototypes rapidly. Participants will learn to leverage modern AI tools, APIs, and frameworks to create functional prototypes that solve real business problems.

Key Focus Areas

Modern AI APIs: OpenAI GPT-4, Claude, Gemini integration and optimization
Rapid Development: Streamlit, Gradio, and no-code platforms for quick prototyping
Production Ready: Security, performance, and scalability considerations
Real Projects: Build functional prototypes during the course

Day 1: Foundations & Rapid Prototyping Tools

AI Prototyping Landscape
- Current AI ecosystem: LLMs, vision models, specialized services
- Open source vs. commercial solutions comparison
- Prototyping vs. production considerations
- Cost optimization and API management strategies
No-Code/Low-Code AI Tools
- Zapier AI, Microsoft Power Platform AI Builder
- Google Cloud AutoML and Bubble.io integrations
- Workshop: Build customer support chatbot
- Deploy and test prototype functionality
API Integration & Orchestration
- LLM API setup: OpenAI, Anthropic Claude, Google
- Prompt engineering best practices
- Multi-modal AI integration (text, image, audio)
- Error handling and fallback strategies

Rapid Development Frameworks
- Streamlit for interactive AI interfaces
- Gradio for ML model demonstrations
- Real-time data visualization techniques
- Lab: Document analysis tool development
Data Integration & Vector Databases
- Vector database setup: Pinecone, Weaviate, ChromaDB
- RAG (Retrieval-Augmented Generation) patterns
- Embedding generation and similarity search
- Workshop: Company knowledge assistant

Day 2: Advanced Prototyping & Production Preparation

Custom Model Development
- Fine-tuning strategies and dataset preparation
- OpenAI and Hugging Face transformers integration
- Model optimization: quantization and pruning
- ONNX conversion for cross-platform deployment
Advanced Integration Patterns
- Multi-agent systems
- Agent orchestration and tool integration
- Function calling and workflow automation
- Project: Code review assistant development

Performance & Monitoring
- Caching strategies for AI responses
- Asynchronous processing and load balancing
- Cost monitoring and performance metrics
- A/B testing AI features and user feedback
Security & Compliance
- Prompt injection prevention techniques
- Data privacy and API credential management
- GDPR compliance and audit trails
- Bias detection and mitigation strategies
Prototype to Production Pipeline
- Containerization with Docker
- Cloud deployment: AWS, GCP, Azure options
- CI/CD pipelines for AI applications
- Production readiness and monitoring setup
Final Project & Presentations
- Team challenge: Build AI prototype for business scenario
- Presentation and peer review session
- Best practices discussion and next steps