This course delves into the intricacies of developing and deploying scalable AI solutions utilizing microservices architecture. Participants will engage in a project-based learning environment that emphasizes hands-on experience and practical application of concepts. By the end of the program, learners will have the opportunity to publish their findings and projects in Cademix Magazine, enhancing their professional visibility and contributing to the broader AI community.
The curriculum is designed to provide comprehensive knowledge and skills necessary for effective AI model deployment and management. Participants will explore various microservices frameworks, cloud integration strategies, and performance optimization techniques. The course culminates in a final project where learners will design and implement a scalable AI solution, demonstrating their acquired expertise in a real-world context.
Introduction to Microservices Architecture
Overview of AI Model Deployment Strategies
Containerization with Docker for AI Applications
Orchestrating Microservices with Kubernetes
API Development and Management for AI Services
Cloud Platforms for Scalable AI Solutions (AWS, Azure, GCP)
Monitoring and Logging in Microservices Environments
Performance Tuning and Optimization Techniques
Security Best Practices in AI Microservices
Final Project: Building a Scalable AI Solution with Microservices