Deploying Open Models

Overview

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.

Syllabus

Containerization for Model Deployment

You’ll package AI models into optimized Docker containers that run consistently across environments. You’ll apply best practices like multi-stage builds, dependency trimming, and GPU runtime configs to reduce overhead and improve portability. You’ll also address security and orchestration basics, giving you the foundation to deploy models reliably in both local and cloud setups.

Cloud Deployment Options and Costs

You'll evaluate real-world deployment options for AI models across major cloud platforms and rapid prototyping environments. You'll compare AWS, GCP, Azure, and Hugging Face Spaces, weighing cost, scalability, compliance, and performance trade-offs across usage-based, reserved, and serverless pricing models. Through hands-on deployment , you'll apply cost modeling frameworks and trace deployment decisions from prototype through production. By the end, you'll be able to choose and justify the right deployment strategy based on budget, regulatory requirements, and production needs.

Monitoring and Maintenance

Learn how to keep deployed models reliable over time through monitoring, logging, and automated testing. You’ll track latency, throughput, and error rates, and set up alerts for performance degradation. You’ll also practice applying version control, update strategies, and regression testing so your models remain stable and trustworthy in production environments.