Description
This project aims to build a complete end-to-end Machine Learning pipeline running entirely on Kubernetes, using Go, and containerized ML components.
The pipeline will automate the lifecycle of a machine learning model, including:
- Data ingestion/collection
- Model training as a Kubernetes Job
- Model artifact storage in an S3-compatible registry (e.g. Minio)
- A Go-based deployment controller that automatically deploys new model versions to Kubernetes using Rancher
- A lightweight inference service that loads and serves the latest model
- Monitoring of model performance and service health through Prometheus/Grafana
The outcome is a working prototype of an MLOps workflow that demonstrates how AI workloads can be trained, versioned, deployed, and monitored using the Kubernetes ecosystem.
Goals
By the end of Hack Week, the project should:
Produce a fully functional ML pipeline running on Kubernetes with:
- Data collection job
- Training job container
- Storage and versioning of trained models
- Automated deployment of new model versions
- Model inference API service
- Basic monitoring dashboards
Showcase a Go-based deployment automation component, which scans the model registry and automatically generates & applies Kubernetes manifests for new model versions.
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.
Resources
Updates
- Training pipeline and datasets
- Inference Service py
Looking for hackers with the skills:
This project is part of:
Hack Week 25
Activity
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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.
I might use qwen3-coder or something similar as a starting point.
Everything would be done 100% offline without network available to the container, since I prefer to see when network is needed, and make it so it's never needed (other than initial downloads).
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- Learn something about RAG, LLM, AI.
- Find out if everything works offline as intended.
- As an end result have a new way to access my own existing know-how, but so that I can query the wisdom in them.
- Be flexible to pivot in any direction, as long as there are new things learned.
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To be found on the fly.
Timeline
Day 1 (of 4)
- Tried out a RAG demo, expanded on feeding it my own data
- Experimented with qwen3-coder to add a persistent chat functionality, and keeping vectors in a pickle file
- Optimizations to keep everything within context window
- Learn and add a bit of PyTest
Day 2
- More experimenting and more data
- Study ChromaDB
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Day 3
- The above RAG is working well enough for demonstration purposes.
- Pivot to trying out OpenCode, configuring local Ollama qwen3-coder there, to analyze the RAG demo.
- Figured out how to configure Ollama template to be usable under OpenCode. OpenCode locally is super slow to just running qwen3-coder alone.
Day 4 (final day)
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- Call it success as after all the agentic AI was working locally.
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Backporting patches using LLM by jankara
Description
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Multi-agent AI assistant for Linux troubleshooting by doreilly
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https://github.com/djoreilly/linux-helper
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Exploring Modern AI Trends and Kubernetes-Based AI Infrastructure by jluo
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The CONCLUSION!!!
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State of the Union document was compiled to summarize lessons learned this week. For more gory details, just read on the diary below! 
Exploring Modern AI Trends and Kubernetes-Based AI Infrastructure by jluo
Description
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Self-Scaling LLM Infrastructure Powered by Rancher
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What if infrastructure scaled itself instead of making you scale it?
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Preparing KubeVirtBMC for project transfer to the KubeVirt organization by zchang
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OpenPlatform Self-Service Portal by tmuntan1
Description
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Resources (SUSE VPN only)
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Advent of Code: The Diaries by amanzini
Description
It was the Night Before Compile Time ...
Hackweek 25 (December 1-5) perfectly coincides with the first five days of Advent of Code 2025. This project will leverage this overlap to participate in the event in real-time.
To add a layer of challenge and exploration (in the true spirit of Hackweek), the puzzles will be solved using a non-mainstream, modern language like Ruby, D, Crystal, Gleam or Zig.
The primary project intent is not just simply to solve the puzzles, but to exercise result sharing and documentation. I'd create a public-facing repository documenting the process. This involves treating each day's puzzle as a mini-project: solving it, then documenting the solution with detailed write-ups, analysis of the language's performance and ergonomics, and visualizations.
|
\ ' /
-- (*) --
>*<
>0<@<
>>>@<<*
>@>*<0<<<
>*>>@<<<@<<
>@>>0<<<*<<@<
>*>>0<<@<<<@<<<
>@>>*<<@<>*<<0<*<
\*/ >0>>*<<@<>0><<*<@<<
___\\U//___ >*>>@><0<<*>>@><*<0<<
|\\ | | \\| >@>>0<*<0>>@<<0<<<*<@<<
| \\| | _(UU)_ >((*))_>0><*<0><@<<<0<*<
|\ \| || / //||.*.*.*.|>>@<<*<<@>><0<<<
|\\_|_|&&_// ||*.*.*.*|_\\db//_
""""|'.'.'.|~~|.*.*.*| ____|_
|'.'.'.| ^^^^^^|____|>>>>>>|
~~~~~~~~ '""""`------'
------------------------------------------------
This ASCII pic can be found at
https://asciiart.website/art/1831
Goals
Code, Docs, and Memes: An AoC Story
Have fun!
Involve more people, play together
Solve Days 1-5: Successfully solve both parts of the Advent of Code 2025 puzzles for Days 1-5 using the chosen non-mainstream language.
Daily Documentation & Language Review: Publish a detailed write-up for each day. This documentation will include the solution analysis, the chosen algorithm, and specific commentary on the language's ergonomics, performance, and standard library for the given task.