今週のCN AI(2024年4月22日)

Key Developments

1. Collaborations & Crossovers Opportunities with other communities

• CNAI Working Group members met with folks from from the AI Alliance to discuss potential collaborations. The AI Alliance focuses on bringing organizations together to address common AI problems and might offer opportunities for surveys and reference architectures.

• Interest in a potential presentation on legal aspects of AI and potential contributions and discussion to spurr in the upcoming weeks on the topic.

• The group is exploring collaboration options with the LF data & AI communitiy to ensure there is alignment and to surface more chances for collaboration from the community.

2. Advancement & Innovations in CNCF AI Technologies

Here are some updates on most recent projects in the space:

• HAMi Project: formerly known as k8s-vGPU-scheduler, is an “all-in-one” chart designed to manage Heterogeneous AI Computing Devices in a k8s cluster. It includes everything you would expect, such as device sharing, device memory control, device type specification, and device UUID specification.

• Feast Project: An LF incubating project focused on creating a vector database for a feature store, initially started with time series data but now expanded to more general purposes.

3. CNAI Group Initiatives & Activities

• Mission, Vision, and FAQs: The CNAI working group is updating its documentation to refine the mission, vision, and success definitions. Updates in the FAQ to distinguish CNAI’s role from other CNCF and AI efforts and explaining its collaboration with external groups like the AI Alliance and OPEA. The document is still open for community feedback, with an ongoing effort to eliminate overlaps with existing charters.

• CNCF YouTube Summarizer: This community-driven project envisions using AI to automatically summarize CNCF YouTube content, enhancing accessibility.

• Challenges Scheduling AI writup: The writeup continues to evolve, incorporating valuable community feedback. Discussions about turning the document into knowledge posts or a longer-form paper are ongoing.

• Models & Finetuning: Student Project: An initiative involving students to fine-tune LLM models using CNCF tooling, aiming for practical training and deployment strategies.

For more information about the state of initiatves, you can find them tracked here.

How to Get Involved

The CNAI Working Group welcomes participation! Here’s how you can contribute:

• Join the conversation: Find the group on the #wg-artificial-intelligence Slack channel.
• Check our onboarding documentation to explore ways to contribute.
• Check the current initiatives, and reach out if you would like contribute and participate!