Intelligent Applications Fundamentals Training in Madrid, Spain

The Intelligent Applications Fundamentals Training introduces participants to the concepts, architectures, and practices for building applications enhanced with Artificial Intelligence.

The course focuses on how AI is embedded into modern software systems to create intelligent, adaptive, and data-driven applications, covering both business and technical perspectives.

Introduction to Intelligent Applications

• What are intelligent applications?
• Evolution from traditional to AI-enabled apps
• Business value of intelligent features
• Common intelligent application examples

Core Components of Intelligent Applications

• User interface and experience
• AI/ML models and services
• Data sources and pipelines
• Decision and automation layers

AI Capabilities in Applications

• Natural language processing
• Computer vision
• Recommendation systems
• Predictive analytics
• Generative AI features

Intelligent Application Architectures

• Cloud-based intelligent apps
• Microservices and AI services
• API-driven AI integration
• Edge vs cloud intelligence

Data Foundations for Intelligent Apps

• Data collection and quality
• Training vs inference data
• Real-time vs batch data
• Data governance and ownership

Model Lifecycle in Applications

• Model training and deployment (high-level)
• Model updates and versioning
• Monitoring model performance
• Managing model drift

AI Feature Design

• Identifying high-value AI features
• Human + AI collaboration design
• Explainability and transparency
• User trust and adoption

Integration of AI into Business Workflows

• Embedding AI into processes
• Decision support vs automation
• Human-in-the-loop patterns
• Exception handling

Performance, Scalability & Reliability

• Latency considerations
• Scaling AI services
• Availability and resilience
• Cost considerations

Security, Privacy & Compliance

• Data protection in AI applications
• Access controls
• Regulatory considerations
• Secure AI service integration

Responsible AI in Applications

• Bias and fairness
• Transparency and explainability
• Accountability and oversight
• Ethical AI principles

Testing & Quality Assurance

• Testing intelligent features
• Validating AI outputs
• Monitoring accuracy and errors
• Continuous improvement

Roadmap for Building Intelligent Applications

• From pilot to production
• Building internal capabilities
• Vendor and platform selection
• Change management

Hands-on Exercises

Summary and Conclusion