Overview: How to run AI models in cloud development environments
Many technical professionals are seeking ways to incorporate new AI capabilities into their workflows to enhance efficiency. But because large corporations maintain these large language models (LLMs), we must be cautious about sharing sensitive information. Developers working in corporate environments need internal access to on-premise LLMs to ensure data is not shared outside the organization.
This is how privately hosted LLMs can help. Organizations can train and host LLMs via internal data to make their employees more productive. This learning path shows you how to create and serve an LLM in a cloud development environment that developers can access from a cloud development workspace. We’ll be showcasing it using a tool called RamaLama, which we will use in the Red Hat OpenShift Dev Spaces cloud development environment.
Prerequisites:
In this learning path, you will:
- Learn to integrate AI models (LLMs) into cloud development environments for efficiency and security.
- Explore RamaLama, an open-source tool for securely and locally serving AI models.
- See how RamaLama excels with Open Container Initiative (OCI) containers for isolated model execution.
- Get a step-by-step guide to deploying an IBM Granite model as a private AI assistant in Red Hat OpenShift Dev Spaces.
- Understand how devfiles configure and secure your cloud-based environment.
