This course delves into the intricacies of deploying artificial intelligence models within cloud infrastructures, equipping participants with essential skills for the modern tech landscape. Through a project-based and interactive approach, learners will engage in hands-on experiences that culminate in a final project, enabling them to apply theoretical knowledge to real-world scenarios. The course emphasizes practical applications, ensuring that participants can confidently deploy AI models in various cloud environments, enhancing their employability and expertise in this critical area.
Participants will explore a comprehensive syllabus that covers cloud service models, deployment strategies, and tools essential for AI model integration. By the end of the course, learners will not only gain technical proficiency but also have the opportunity to publish their results in Cademix Magazine, showcasing their work to a broader audience. This unique blend of practical training and publication encourages both professional growth and community engagement.
Understanding cloud computing fundamentals and AI model requirements
Exploring different cloud service models: IaaS, PaaS, and SaaS
Setting up cloud environments for AI model deployment
Utilizing containerization technologies (e.g., Docker, Kubernetes)
Implementing continuous integration/continuous deployment (CI/CD) pipelines
Monitoring and maintaining deployed AI models in the cloud
Scaling AI models for performance optimization
Leveraging cloud-native tools for data processing and storage
Conducting cost analysis and resource management in cloud deployments
Final project: Deploying an AI model in a chosen cloud environment and presenting results