Deploying Open Models

The Deploying Open Models course is designed for developers, engineers, and technical product builders who are new to Generative AI but already have intermediate machine learning knowledge, basic Python proficiency, and familiarity with development environments such as Visual Studio Code (VS Code), and who want to engineer, customize, and deploy open generative AI solutions while avoiding vendor lock-in. The course teaches learners how to package, host, and maintain generative AI models in real-world production environments. The course begins with Docker containerization, where learners design optimized Dockerfiles, apply dependency management techniques, and implement security practices such as isolation and access control. Next, learners explore cloud deployment strategies, comparing options across Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, and specialized providers, while also evaluating cost, performance, and compliance considerations. They will also gain hands-on experience with rapid prototyping on Hugging Face Spaces and learn about serverless architectures for efficiency. In the final module, the focus shifts to monitoring and maintenance, where learners implement logging systems, performance dashboards, alerting frameworks, and version control practices to ensure reliable long-term operations. By the end of the course, learners will have deployed an open model with comprehensive monitoring, security, and update management in place

• Design optimized Dockerfiles and apply dependency management
• Implement security practices such as container isolation and access control
• Explore cloud deployment strategies and evaluate providers based on cost, performance, and compliance

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work