GenAI infrastructure to take your models to production
Bring your GenAI projects to market faster with expert guidance and an end-to-end infrastructure stack.
With security built-in at the hardware level through confidential AI, enhanced model outputs via RAG, and a full stack for LLM implementation, Canonical enables you to take GenAI to production.
Learn to launch open source LLMs in practice in 5 days
Start by enabling your team
The hardest part of any GenAI project is bringing your large language model (LLM) to production.
In a 5-day MLOps workshop, our experts will help you upskill your team, define your GenAI architecture, optimize LLMs in practice, and put you on the fast-track to production.
Scale up your project with optimized GenAI infrastructure
A fully open source GenAI infrastructure stack
Canonical delivers integrated solutions spanning the entire machine learning lifecycle, including:
- Bare metal and cluster provisioning for AI hardware
- OpenStack private cloud infrastructure
- Kubernetes for container orchestration
- Inference engines to run your LLMs
- An MLOps platform for pipeline and workload automation
- Big data streaming tooling
- A vector database to optimize speed and performance
We can support you at any stage in your GenAI journey. Get started on your local machine with Ollama, then scale up to the full stack.
Enhance your LLM outputs with RAG
Ensure accuracy and precision
Retrieval augmented generation (RAG) can enhance the accuracy and relevance of your LLM outputs by enabling the model to reference additional sources outside of its training data — for example, your organization's knowledge base.
Equip your team with the knowledge, tools, and architecture for seamless RAG implementation through Canonical's RAG workshop.
Secure your data and models with confidential AI
Start with security at the hardware level
Canonical has 20 years of experience securing open source software, and we are committed to helping you protect your GenAI projects.
Your GenAI models and sensitive data need to be secured at every stage of the ML lifecycle. This is especially true if you operate in a highly regulated industry, or if you are augmenting your LLM with proprietary data via RAG.
With confidential AI on Ubuntu, you can protect your AI workloads at run-time with a hardware-rooted execution environment. Strengthen your compliance posture and safely fine-tune your model with your enterprise data.
Learn more about GenAI infrastructure
Read the blog for an in-depth exploration of GenAI fundamentals.
Secure your AI workloads with Confidential VMs
Dive deeper into confidential AI in our webinar.
LLMs and RAG with Charmed OpenSearch
Explore how to optimize your GenAI initiatives with Canonical's OpenSearch.