Artificial Intelligence playground for Data Scientist
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.
Looking for hackers with the skills:
ai artificial-intelligence machinelearning prototype agile projectmanagement innovation
This project is part of:
Hack Week 19
Activity
Comments
-
almost 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?
-
almost 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.
-
-
almost 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.
-
almost 6 years ago by bmwiedemann | Reply
If you have a need for this project for 2x NVIDIA Tesla T4, 16GB - ping me.
-
-
almost 6 years ago by rsblendido | Reply
Is this about Kubeflow?
-
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.
-
-
Similar Projects
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 Python web server (e.g., using FastAPI) 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
Bugzilla goes AI - Phase 1 by nwalter
Description
This project, Bugzilla goes AI, aims to boost developer productivity by creating an autonomous AI bug agent during Hackweek. The primary goal is to reduce the time employees spend triaging bugs by integrating Ollama to summarize issues, recommend next steps, and push focused daily reports to a Web Interface.
Goals
To reduce employee time spent on Bugzilla by implementing an AI tool that triages and summarizes bug reports, providing actionable recommendations to the team via Web Interface.
Project Charter
https://docs.google.com/document/d/1HbAvgrg8T3pd1FIx74nEfCObCljpO77zz5In_Jpw4as/edit?usp=sharing## Description
Gemini-Powered Socratic Bug Evaluation and Management Assistant by rtsvetkov
Description
To build a tool or system that takes a raw bug report (including error messages and context) and uses a large language model (LLM) to generate a series of structured, Socratic-style questions designed to guide a the integration and development toward the root cause, rather than just providing a direct, potentially incorrect fix.
Goals
Set up a Python environment
Set the environment and get a Gemini API key. 2. Collect 5-10 realistic bug reports (from open-source projects, personal projects, or public forums like Stack Overflow—include the error message and the initial context).
Build the Dialogue Loop
- Write a basic Python script using the Gemini API.
- Implement a simple conversational loop: User Input (Bug) -> AI Output (Question) -> User Input (Answer to AI's question) -> AI Output (Next Question). Code Implementation
Socratic Strategy Implementation
- Refine the logic to ensure the questions follow a Socratic path (e.g., from symptom-> context -> assumptions -> root cause).
- Implement Function Calling (an advanced feature of the Gemini API) to suggest specific actions to the user, like "Run a ping test" or "Check the database logs."
Resources
Update M2Crypto by mcepl
There are couple of projects I work on, which need my attention and putting them to shape:
Goal for this Hackweek
- Put M2Crypto into better shape (most issues closed, all pull requests processed)
- More fun to learn jujutsu
- Play more with Gemini, how much it help (or not).
- Perhaps, also (just slightly related), help to fix vis to work with LuaJIT, particularly to make vis-lspc working.
MCP Server for SCC by digitaltomm
Description
Provide an MCP Server implementation for scc.suse.com. Similar to the organization APIs, this can expose to customers data about their subscriptions, orders, systems and products. Authentication should be done by organization credentials, similar to what needs to be provided to RMT/MLM.
Goals
We want to show a proof of concept to connect to the SCC MCP server with any agentic AI, like gemini-cli, copilot or Claude desktop. This would enable the user to ask questions regarding their SCC inventory, like "When do I need to re-new my SLES subscription", "Do I have active systems running on unsupported operating systems?".
The user can decide themselves to use any AI backend which they trust to use for this.
Resources
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 Python web server (e.g., using FastAPI) 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