Project here: https://confluence.suse.com/display/AAI/HackWeek19 Will keep working out of HackWeek as "best effort" personal project to make it evolve and keep learning.

What this project is about?

Data Scientist ofter starts working on their laptop before moving into company resources. As in many other cases they have to solve many challenges by themselves before actually start working on "their stuff". The idea is to build a prototype we will eventually try to evolve in a product that answers the following pre-requisites:

  • Rapid Time to work: I, as Data Scientist or Data Engineer, need to install the playground quickly and be ready to work
  • Everything at the right place: I as Data Scientist or Data Engineer want an easy way to find things and use them
  • No time to waste: I as Data Scientist or Data Engineer want to be able to replicate the model synchronizing it with another infrastructure through a "click and done" model
  • No complexity rule: I as Data Scientist or Data Engineer want to avoid waste time in complex configurations or debug things. Complexity needs to hided to me

Project Team requirements

Because this is a first attempt to prototype I have to ask for some "not official" rules to be applied:

  • Max 7/9 people in the team with a max of 3 Engineers
  • If you apply you have to make yourself available from 10 am to 5 pm CET (if you're on a different time zone you have to consider we'll have a lot of team discussion so could be challenging)
  • This is a 5 days sprint approach where everyone needs to be open, collaborative, bold, creative.

FAQ

  • I'm not an engineer or an expert: Great this project require (possibly) at least 1 person from marketing, sales-engineering, services, support
  • Am I required to code?: No, but you're required to share your ideas and views, while the end goal is to build a prototype (that's why we need a couple of engineers) the scope is to have something to show and demonstrate we may build something useful for the Data Scientist community
  • Woah this seems to be a super serious project: Nah it's a fun experiment to learn how much we may push our limit through rapid prototyping and "be different"
  • So how do I signup?: easy just join the team here on hackweek and/or contact me alessandro.festa@suse.com for further details.

This project is part of:

Hack Week 19

Activity

  • almost 6 years ago: jordimassaguerpla liked this project.
  • almost 6 years ago: rsblendido joined this project.
  • almost 6 years ago: jeffpr joined this project.
  • almost 6 years ago: FSzekely liked this project.
  • almost 6 years ago: bfilho left this project.
  • almost 6 years ago: bfilho joined this project.
  • almost 6 years ago: bfromme liked this project.
  • almost 6 years ago: bfromme joined this project.
  • almost 6 years ago: rsblendido liked this project.
  • about 6 years ago: gboiko liked this project.
  • about 6 years ago: afesta added keyword "innovation" to this project.
  • about 6 years ago: afesta added keyword "projectmanagement" to this project.
  • about 6 years ago: afesta added keyword "ai" to this project.
  • about 6 years ago: afesta added keyword "artificial-intelligence" to this project.
  • about 6 years ago: afesta added keyword "machinelearning" to this project.
  • about 6 years ago: afesta added keyword "prototype" to this project.
  • about 6 years ago: afesta added keyword "agile" to this project.
  • about 6 years ago: afesta liked this project.
  • about 6 years ago: afesta started this project.
  • about 6 years ago: afesta originated this project.

  • Comments

    • hennevogel
      about 6 years ago by hennevogel | Reply

      Can you explain what kind of output you would expect? Like an application? A set of packages? Some IaC description?

      • afesta
        about 6 years ago by afesta | Reply

        This is something we have to decide during the hack week, usually a prototype based on a target of the challenge decided by the team. If this will be simple artifacts made of a sum of existing items, an application or a set of packages has to be decided. The scope is to foster innovation under a very fast cycle (5 days) and get a result that allows us to learn if: is doable, what we need to address to make it a real product and how long could take. Don't expect huge development or impossible challenges, this is about pure innovation and ideas.. and build a way to demonstrate our idea.

    • afesta
      about 6 years ago by afesta | Reply

      This is something we have to decide during the hack week, usually a prototype based on a target of the challenge decided by the team. If this will be simple artifacts made of a sum of existing items, an application or a set of packages has to be decided. The scope is to foster innovation under a very fast cycle (5 days) and get a result that allows us to learn if: is doable, what we need to address to make it a real product and how long could take. Don't expect huge development or impossible challenges, this is about pure innovation and ideas.. and build a way to demonstrate our idea.

    • bmwiedemann
      about 6 years ago by bmwiedemann | Reply

      If you have a need for this project for 2x NVIDIA Tesla T4, 16GB - ping me.

      • afesta
        about 6 years ago by afesta | Reply

        So cool! To be honest I'll like more to use your brain for the project...willing to give me a chance and have fun for a week with this crazy PM?

    • rsblendido
      almost 6 years ago by rsblendido | Reply

      Is this about Kubeflow?

      • afesta
        almost 6 years ago by afesta | Reply

        Could be. I mean the only "constraint" is that ideally should work on a laptop and Kubeflow works on K8's but if you use something like MLRun you may overcome many challenges. The ultimate goal of the project is to provide Data scientists a playground so that they do not need to learn and install and configure everything but it's easy enough to start from your laptop (and eventually) move it to a server/cloud environment.

    • jeffpr
      almost 6 years ago by jeffpr | Reply

      @afesta : I will be working with you for the SUSEcon demos - just thought I would hop in here when I can.

      • afesta
        almost 6 years ago by afesta | Reply

        Cool!

    Similar Projects

    Enable more features in mcp-server-uyuni by j_renner

    Description

    I would like to contribute to mcp-server-uyuni, the MCP server for Uyuni / Multi-Linux Manager) exposing additional features as tools. There is lots of relevant features to be found throughout the API, for example:

    • System operations and infos
    • System groups
    • Maintenance windows
    • Ansible
    • Reporting
    • ...

    At the end of the week I managed to enable basic system group operations:

    • List all system groups visible to the user
    • Create new system groups
    • List systems assigned to a group
    • Add and remove systems from groups

    Goals

    • Set up test environment locally with the MCP server and client + a recent MLM server [DONE]
    • Identify features and use cases offering a benefit with limited effort required for enablement [DONE]
    • Create a PR to the repo [DONE]

    Resources


    Kubernetes-Based ML Lifecycle Automation by lmiranda

    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:

    1. 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
    2. Showcase a Go-based deployment automation component, which scans the model registry and automatically generates & applies Kubernetes manifests for new model versions.

    3. Enable continuous improvement by making the system modular and extensible (e.g., additional models, metrics, autoscaling, or drift detection can be added later).

    4. Prepare a short demo explaining the end-to-end process and how new models flow through the system.

    Resources

    Project Repository

    Updates

    1. Training pipeline and datasets
    2. Inference Service py


    MCP Trace Suite by r1chard-lyu

    Description

    This project plans to create an MCP Trace Suite, a system that consolidates commonly used Linux debugging tools such as bpftrace, perf, and ftrace.

    The suite is implemented as an MCP Server. This architecture allows an AI agent to leverage the server to diagnose Linux issues and perform targeted system debugging by remotely executing and retrieving tracing data from these powerful tools.

    • Repo: https://github.com/r1chard-lyu/systracesuite
    • Demo: Slides

    Goals

    1. Build an MCP Server that can integrate various Linux debugging and tracing tools, including bpftrace, perf, ftrace, strace, and others, with support for future expansion of additional tools.

    2. Perform testing by intentionally creating bugs or issues that impact system performance, allowing an AI agent to analyze the root cause and identify the underlying problem.

    Resources

    • Gemini CLI: https://geminicli.com/
    • eBPF: https://ebpf.io/
    • bpftrace: https://github.com/bpftrace/bpftrace/
    • perf: https://perfwiki.github.io/main/
    • ftrace: https://github.com/r1chard-lyu/tracium/


    Liz - Prompt autocomplete by ftorchia

    Description

    Liz is the Rancher AI assistant for cluster operations.

    Goals

    We want to help users when sending new messages to Liz, by adding an autocomplete feature to complete their requests based on the context.

    Example:

    • User prompt: "Can you show me the list of p"
    • Autocomplete suggestion: "Can you show me the list of p...od in local cluster?"

    Example:

    • User prompt: "Show me the logs of #rancher-"
    • Chat console: It shows a drop-down widget, next to the # character, with the list of available pod names starting with "rancher-".

    Technical Overview

    1. The AI agent should expose a new ws/autocomplete endpoint to proxy autocomplete messages to the LLM.
    2. The UI extension should be able to display prompt suggestions and allow users to apply the autocomplete to the Prompt via keyboard shortcuts.

    Resources

    GitHub repository


    Local AI assistant with optional integrations and mobile companion by livdywan

    Description

    Setup a local AI assistant for research, brainstorming and proof reading. Look into SurfSense, Open WebUI and possibly alternatives. Explore integration with services like openQA. There should be no cloud dependencies. Mobile phone support or an additional companion app would be a bonus. The goal is not to develop everything from scratch.

    User Story

    • Allison Average wants a one-click local AI assistent on their openSUSE laptop.
    • Ash Awesome wants AI on their phone without an expensive subscription.

    Goals

    • Evaluate a local SurfSense setup for day to day productivity
    • Test opencode for vibe coding and tool calling

    Timeline

    Day 1

    • Took a look at SurfSense and started setting up a local instance.
    • Unfortunately the container setup did not work well. Tho this was a great opportunity to learn some new podman commands and refresh my memory on how to recover a corrupted btrfs filesystem.

    Day 2

    • Due to its sheer size and complexity SurfSense seems to have triggered btrfs fragmentation. Naturally this was not visible in any podman-related errors or in the journal. So this took up much of my second day.

    Day 3

    Day 4

    • Context size is a thing, and models are not equally usable for vibe coding.
    • Through arduous browsing for ollama models I did find some like myaniu/qwen2.5-1m:7b with 1m but even then it is not obvious if they are meant for tool calls.

    Day 5

    • Whilst trying to make opencode usable I discovered ramalama which worked instantly and very well.

    Outcomes

    surfsense

    I could not easily set this up completely. Maybe in part due to my filesystem issues. Was expecting this to be less of an effort.

    opencode

    Installing opencode and ollama in my distrobox container along with the following configs worked for me.

    When preparing a new project from scratch it is a good idea to start out with a template.

    opencode.json

    ``` {


    SUSE Observability MCP server by drutigliano

    Description

    The idea is to implement the SUSE Observability Model Context Protocol (MCP) Server as a specialized, middle-tier API designed to translate the complex, high-cardinality observability data from StackState (topology, metrics, and events) into highly structured, contextually rich, and LLM-ready snippets.

    This MCP Server abstract the StackState APIs. Its primary function is to serve as a Tool/Function Calling target for AI agents. When an AI receives an alert or a user query (e.g., "What caused the outage?"), the AI calls an MCP Server endpoint. The server then fetches the relevant operational facts, summarizes them, normalizes technical identifiers (like URNs and raw metric names) into natural language concepts, and returns a concise JSON or YAML payload. This payload is then injected directly into the LLM's prompt, ensuring the final diagnosis or action is grounded in real-time, accurate SUSE Observability data, effectively minimizing hallucinations.

    Goals

    • Grounding AI Responses: Ensure that all AI diagnoses, root cause analyses, and action recommendations are strictly based on verifiable, real-time data retrieved from the SUSE Observability StackState platform.
    • Simplifying Data Access: Abstract the complexity of StackState's native APIs (e.g., Time Travel, 4T Data Model) into simple, semantic functions that can be easily invoked by LLM tool-calling mechanisms.
    • Data Normalization: Convert complex, technical identifiers (like component URNs, raw metric names, and proprietary health states) into standardized, natural language terms that an LLM can easily reason over.
    • Enabling Automated Remediation: Define clear, action-oriented MCP endpoints (e.g., execute_runbook) that allow the AI agent to initiate automated operational workflows (e.g., restarts, scaling) after a diagnosis, closing the loop on observability.

     Hackweek STEP

    • Create a functional MCP endpoint exposing one (or more) tool(s) to answer queries like "What is the health of service X?") by fetching, normalizing, and returning live StackState data in an LLM-ready format.

     Scope

    • Implement read-only MCP server that can:
      • Connect to a live SUSE Observability instance and authenticate (with API token)
      • Use tools to fetch data for a specific component URN (e.g., current health state, metrics, possibly topology neighbors, ...).
      • Normalize response fields (e.g., URN to "Service Name," health state DEVIATING to "Unhealthy", raw metrics).
      • Return the data as a structured JSON payload compliant with the MCP specification.

    Deliverables

    • MCP Server v0.1 A running Golang MCP server with at least one tool.
    • A README.md and a test script (e.g., curl commands or a simple notebook) showing how an AI agent would call the endpoint and the resulting JSON payload.

    Outcome A functional and testable API endpoint that proves the core concept: translating complex StackState data into a simple, LLM-ready format. This provides the foundation for developing AI-driven diagnostics and automated remediation.

    Resources

    • https://www.honeycomb.io/blog/its-the-end-of-observability-as-we-know-it-and-i-feel-fine
    • https://www.datadoghq.com/blog/datadog-remote-mcp-server
    • https://modelcontextprotocol.io/specification/2025-06-18/index
    • https://modelcontextprotocol.io/docs/develop/build-server

     Basic implementation

    • https://github.com/drutigliano19/suse-observability-mcp-server

    Results

    Successfully developed and delivered a fully functional SUSE Observability MCP Server that bridges language models with SUSE Observability's operational data. This project demonstrates how AI agents can perform intelligent troubleshooting and root cause analysis using structured access to real-time infrastructure data.

    Example execution


    Song Search with CLAP by gcolangiuli

    Description

    Contrastive Language-Audio Pretraining (CLAP) is an open-source library that enables the training of a neural network on both Audio and Text descriptions, making it possible to search for Audio using a Text input. Several pre-trained models for song search are already available on huggingface

    SUSE Hackweek AI Song Search

    Goals

    Evaluate how CLAP can be used for song searching and determine which types of queries yield the best results by developing a Minimum Viable Product (MVP) in Python. Based on the results of this MVP, future steps could include:

    • Music Tagging;
    • Free text search;
    • Integration with an LLM (for example, with MCP or the OpenAI API) for music suggestions based on your own library.

    The code for this project will be entirely written using AI to better explore and demonstrate AI capabilities.

    Result

    In this MVP we implemented:

    • Async Song Analysis with Clap model
    • Free Text Search of the songs
    • Similar song search based on vector representation
    • Containerised version with web interface

    We also documented what went well and what can be improved in the use of AI.

    You can have a look at the result here:

    Future implementation can be related to performance improvement and stability of the analysis.

    References


    Review SCC team internal development processes by calmeidadeoliveira

    Description

    Continue with the Hackweek 2024, with focus on reviewing existing processes / ways of working and creating workflows:

    Goals

    • Check all the processes from [1] and [3]
    • move them to confluence [4], make comments, corrections, etc.
    • present the result to the SCC team and ask for reviews

    Resources

    [1] https://github.com/SUSE/scc-docs/blob/master/team/workflow/kanban-process.md [2] https://github.com/SUSE/scc-docs/tree/master/team [3] https://github.com/SUSE/scc-docs/tree/master/team/workflow

    Confluence

    [4] https://confluence.suse.com/spaces/scc/pages/1537703975/Processes+and+ways+of+working