Course Description:
This course guides learners through selecting and implementing a comprehensive technology stack for AI agent development and deployment. Topics include frontend and backend technologies, data management, messaging systems, and infrastructure automation for scalable, maintainable solutions.
Lecture 1: Comprehensive Tech Stack Selection
Explore the core technologies powering modern AI systems, including frontend frameworks, backend languages, databases, message queues, and monitoring tools for full-stack development.
Key Objectives:
- Frontend: React, Vue, Svelte for user interfaces
- Backend: Node.js, Python, Go for agent hosting
- Database: PostgreSQL, MongoDB, Redis for data management
- Message Queue: RabbitMQ, Apache Kafka for communication
- Monitoring: Prometheus, Grafana for performance tracking
Lecture 2: Infrastructure as Code
Learn to automate AI infrastructure deployment and management using containerization, orchestration, and CI/CD pipelines. Implement monitoring and scaling best practices for production environments.
Key Objectives:
- Docker containerization for agents
- Kubernetes orchestration
- CI/CD pipelines for agent deployment
- Infrastructure monitoring and scaling
