Understanding Open AI Workspaces

The Understanding Open AI Workspaces course is for developers with intermediate machine learning experience and Python skills who are new to Generative AI and want to learn how to build, customize, optimize, and deploy open source large language models. This course provides learners with the skills to set up, configure, and manage environments for open generative AI development. Beginning with local installations, learners practice running large language models on their own machines using Ollama, exploring performance optimization techniques for consumer hardware, and integrating external applications through APIs. The course then introduces Docker and Docker Compose, guiding learners through containerized environments that ensure reproducibility, persistence, and scalability. Learners build multi-container architectures to separate models and services while managing GPU passthrough and memory optimization. Finally, the course covers Google Colab for cloud-based GPU access, where learners configure free resources, manage storage through Google Drive, and monitor performance within session constraints. By the end, learners will have set up both local and cloud environments, documented their processes, and gained the ability to choose the right workspace for different AI workloads

• Build open source large language models
• Customize and optimize model performance on local hardware
• Set up and configure open generative AI workspaces
• Integrate external applications through APIs
• Use Docker and Docker Compose for reproducible and scalable environments

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