Introducing feature requests: share your ideas on advancing Nebius AI
Introducing feature requests: share your ideas on advancing Nebius AI
There’s a new, convenient way to share ideas for advancing our AI-centric cloud, discuss them with us and the community, and get upvotes from others interested. Introducing the place for requesting Nebius AI features.
Why share ideas? Because by doing so, you enhance not only your own experience using the platform but also the workflows of fellow practitioners in the industry!
Here’s how to request a feature:
- Explore ideas that are already shared on the portal. Upvote those that would help you make your AI pipelines more efficient.
- Hit “Create” and suggest your own idea, like a new product for our platform, a new component for one of the existing services, a new tool for the Marketplace, or more ways to deploy tools — whatever comes to mind. Think about how other folks with different workloads can benefit from this new feature.
- See if the idea resonates with others — if so, they might upvote it and suggest additional thoughts in the comments. You can engage in the conversation as well.
- Our team discusses internally and provides updates on each incoming idea.
- The more ideas you suggest, the higher you will rank on the leaderboard on the right.
Let’s build the best GPU cloud together: ideas.nebius.ai.
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See also
In May, we published a guide on using DVC in Nebius, discussed Slurm vs K8s and fundamentals of LoRA, picked the best GPUs for CV, hosted a webinar on applying RAG — and welcomed our tech partner Weights & Biases with its Launch agent in Marketplace.
Discover the latest addition to Marketplace provided by W&B. The Launch agent, a tool for handling ML experiments, can be deployed right away. This marks the beginning of a broader tech partnership between Nebius and W&B, with more outcomes on the way.
Scaling your machine learning workloads will eventually require resource orchestration. This article compares the most popular options today — Slurm and Kubernetes, covering their design origins, ML adaptations and other factors to consider.