⭐ New features

Here is a quick summary of the major updates in the platform that are relevant for users. Minor updates to the documentation will still be performed continuously but not notified here.

📘 docs, 📈 dashboard, 📌 others

• 📌 we started supporting the AI4Life project (2024-09-15)

• 📈 new Profile section where you can link your storage providers (2024-09-04)

• 📈 new try-me deployments (2024-09-03)

• 📌 new federated cluster is available in production (2024-08-05)

• 📈 new external datasets download feature (2024-06-30)

• 📘 new guides to perform Incremental Learning (2024-06-20)

• 📈 new stats section in the Dashboard (2024-05-14)

• 📘 support for token authentication in Federated Learning trainings (2024-04-16)

• 📘 new New features page (meta!) (2024-04-03)

• 📘 new tutorials for creating AI Inference pipelines (Elyra, Flowfuse) (2024-03-18)

• 📘 new MLflow tutorials (2024-02-09)

• 📘 new Frequently Asked Questions (FAQ) page (2024-02-05)

• 📌 MLflow available under new domain (2024-01-29)

• 📈 we enforce a quota of 2 GPUs per user for better resource distribution (2024-01-25)

• 📘 updated Quickstart page (2024-01-24)

• 📈 users are able to select specific GPU models (2024-01-18)

• 📌 new Nextcloud storage available (2024-01-15)

🚀 Upcoming features

In this section, we will offer some insight on to where the AI4OS stack is heading.

First, the are a number of mature features that are in the process of being integrated in the stack:

• 📈 inference endpoints with OSCAR

• 📈 inference endpoints with Nomad

• 📈 try model endpoints with OSCAR

• 📈 email notifications for training deployments that take a longer to deploy

• 📈 new tool for image annotation (CVAT)

• 📈 new tool for efficient video streaming (Kafka)

Along with these upcoming features, we are exploring more improvements. These are in an experimental status and might not finally get integrated in the stack.

• 📈 carbon footprint estimator for your deployments

• 📈 graph visualizations of your module/deployment metadata

• 📈 ability to pause deployments

• 📌 using volumes in deployments to better share data across deployments

Do you find something missing? Please check how to suggest a new feature.