Description
A prerequisite for running applications in a cloud environment is the presence of a container registry. Another common scenario is users performing machine learning workloads in such environments. However, these types of workloads require dedicated infrastructure to run properly. We can leverage these two facts to help users save resources by storing their machine learning models in OCI registries, similar to how we handle some WebAssembly modules. This approach will save users the resources typically required for a machine learning model repository for the applications they need to run.
Goals
Allow PyTorch users to save and load machine learning models in OCI registries.
Resources
This project is part of:
Hack Week 24
Activity
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m=models/gpt-oss-20b-mxfp4.gguf
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benchmark
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Extended private brain - RAG my own scripts and data into offline LLM AI by tjyrinki_suse
Description
For purely studying purposes, I'd like to find out if I could teach an LLM some of my own accumulated knowledge, to use it as a sort of extended brain.
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Day 4 (final day)
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Kubernetes-Based ML Lifecycle Automation by lmiranda
Description
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Enable continuous improvement by making the system modular and extensible (e.g., additional models, metrics, autoscaling, or drift detection can be added later).
Prepare a short demo explaining the end-to-end process and how new models flow through the system.
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Goals
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Q4 2025 CNCF Technology Landscape Radar report:
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- https://www.cncf.io/wp-content/uploads/2025/11/cncfreporttechradar_111025a.pdf
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Description
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Resources