AI-Powered Development

1. Evolution of AI systems from rule-based to generative models
2. Key domains within AI
   1. Machine Learning and Deep Learning
   2. Natural Language Processing
   3. Computer Vision
   4. Generative AI and Large Language Models
3. Understanding model behavior and limitations
4. AI system components
   1. Data pipelines
   2. Model training vs inference
   3. API-driven AI consumption
5. Overview of modern AI ecosystems
   1. Cloud AI providers
   2. Open-source and on-premise AI engines

1. AI-assisted coding workflows
   1. Code generation and completion
   2. Debugging and refactoring assistance
2. Designing AI-enhanced development pipelines
   1. CI/CD augmentation with AI
   2. Automated documentation generation
3. Prompt engineering for developers
   1. Structuring prompts for code and system design
   2. Controlling output consistency and reliability
4. Integrating AI into daily developer tools
   1. IDE integrations
   2. CLI-based AI workflows
5. Evaluating productivity gains and risks

1. Principles of microservices in AI-driven systems
2. Service decomposition strategies
3. API-first design for AI services
4. Stateless vs stateful AI services
5. Containerization and deployment considerations on Linux
6. Inter-service communication patterns
   1. REST
   2. Event-driven architecture

1. Designing RESTful AI endpoints
2. Structuring FastAPI applications for scalability
3. Handling inference requests and responses
4. Integrating pre-trained models and external AI APIs
5. Managing concurrency and performance
6. Logging, monitoring, and observability

1. Creating AI-enabled endpoints in ASP.NET
2. Consuming AI APIs in C# applications
3. Java-based microservices for AI orchestration
4. Cross-language service communication
5. Security considerations in AI API consumption
6. Authentication and authorization for AI services

1. Designing UI for AI-powered applications
2. Calling backend AI services from JS based systems
3. Handling asynchronous AI responses
4. Visualizing AI-generated insights
5. Managing user interaction with AI systems

1. Structuring data for AI processing
2. Sending structured and unstructured data to AI services
3. Interpreting AI outputs for analytics
4. Building pipelines for continuous data analysis
5. Handling large datasets and streaming data
6. Integrating AI insights into business workflows

1. Introduction to Kafka in AI architectures
2. Designing event streams for AI processing
3. Producer and consumer patterns
4. Real-time AI inference pipelines
5. Scaling AI workloads using Kafka
6. Fault tolerance and message reliability

1. Understanding agentic AI systems
   1. Autonomous decision-making
   2. Task decomposition and execution
2. Designing agents for enterprise workflows
3. Processing work orders and documents
   1. Text extraction and parsing
   2. Context-aware decision making
4. Multi-step AI workflows
   1. Chaining AI services
   2. Feedback loops and state management
5. Human-in-the-loop systems
6. Monitoring and controlling agent behavior

1. Benefits and challenges of on-prem AI
2. Setting up local AI inference engines
3. Hardware and infrastructure considerations
4. Model hosting and optimization
5. Data privacy and compliance
6. Hybrid architectures combining cloud and on-prem AI

1. Designing a full AI-powered microservices system
2. Combining Angular, ASP.NET, FastAPI, Kafka
3. End-to-end data flow design
4. Scalability and performance considerations
5. Security and governance in AI systems
6. Deployment strategies on Linux environments
7. Observability, logging, and maintenance

This course is structured to ensure developers not only understand AI conceptually but can also build, integrate, and deploy AI-powered systems that reflect real-world enterprise demands. The emphasis throughout is on practical architecture, production readiness, and leveraging AI as a core engineering capability rather than an experimental add-on.