This course is beneficial to any developer, technical professional, or product owner who is working in a software development or digital transformation environment wanting to grow and advance their knowledge of API development and AI integration, and fast track their career. The aim of each session is to equip delegates with practical skills and knowledge to design, build, and deploy AI-powered ap
→ What makes up an API, i.e., the core components (endpoints, requests, responses) needed to connect systems.
→ The distinction between REST, SOAP, and GraphQL API architectures.
→ API components across the application lifecycle, i.e., design, development, and deployment.
→ API components to further understand how data is exchanged, structured, and secured between systems.
→ Common industry standards for RESTful API design (resources, methods, status codes).
→ Common methodologies applied to structure scalable and maintainable API endpoints.
→ Versioning and documentation techniques to ensure long-term API usability.
→ Common authentication methods including API keys, OAuth 2.0, and token-based access.
→ Common practices applied to secure endpoints against unauthorized access and abuse.
→ Rate limiting and encryption techniques to protect data in transit.
→ Common components of the OpenAI API, i.e., models, endpoints, and request parameters.
→ Common methodologies applied to send requests and handle responses efficiently.
→ Token management and cost-control techniques for production use.
→ Common prompt structures applied to achieve consistent and reliable outputs.
→ Common techniques for managing context, memory, and conversation state.
→ Function calling and structured output techniques to connect ChatGPT with backend systems.
→ Common architectures applied to integrate ChatGPT into existing software systems.
→ Common use cases including chatbots, automation workflows, and intelligent data processing.
→ Error handling and fallback techniques to ensure application reliability.
→ Common testing methodologies applied to validate API and AI response accuracy.
→ Common deployment practices for scaling AI-powered applications in production.
→ Monitoring and performance optimization techniques to maintain reliability at scale.
→ LEORON Certificate
This training is designed for: Software Developers, Backend Engineers and API Architects; Product Managers and Technical Leads; Data and AI Engineers; and Solution Architects, QA Engineers and IT professionals looking to integrate AI capabilities into existing systems.