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 5 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 5 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 5 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 5 years ago by bmwiedemann | Reply
If you have a need for this project for 2x NVIDIA Tesla T4, 16GB - ping me.
-
-
almost 5 years ago by rsblendido | Reply
Is this about Kubeflow?
-
almost 5 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
Use AI tools to convert legacy perl scripts to bash by nadvornik
Description
Use AI tools to convert legacy perl scripts to bash
Goals
Uyuni project contains legacy perl scripts used for setup. The perl dependency could be removed, to reduce the container size. The goal of this project is to research use of AI tools for this task.
Resources
Results:
Aider is not the right tool for this. It works ok for small changes, but not for complete rewrite from one language to another.
I got better results with direct API use from script.
Use local/private LLM for semantic knowledge search by digitaltomm
Description
Use a local LLM, based on SUSE AI (ollama, openwebui) to power geeko search (public instance: https://geeko.port0.org/).
Goals
Build a SUSE internal instance of https://geeko.port0.org/ that can operate on internal resources, crawling confluence.suse.com, gitlab.suse.de, etc.
Resources
Repo: https://github.com/digitaltom/semantic-knowledge-search
Public instance: https://geeko.port0.org/
Results
Internal instance:
I have an internal test instance running which has indexed a couple of internal wiki pages from the SCC team. It's using the ollama (llama3.1:8b) backend of suse-ai.openplatform.suse.com to create embedding vectors for indexed resources and to create a chat response. The semantic search for documents is done with a vector search inside of sqlite, using sqlite-vec.
ghostwrAIter - a local AI assisted tool for helping with support cases by paolodepa
Description
This project is meant to fight the loneliness of the support team members, providing them an AI assistant (hopefully) capable of scraping supportconfigs in a RAG fashion, trying to answer specific questions.
Goals
- Setup an Ollama backend, spinning one (or more??) code-focused LLMs selected by license, performance and quality of the results between:
- deepseek-coder-v2
- dolphin-mistral
- starcoder2
- (...others??)
- Setup a Web UI for it, choosing an easily extensible and customizable option between:
- Extend the solution in order to be able to:
- Add ZIU/Concord shared folders to its RAG context
- Add BZ cases, splitted in comments to its RAG context
- A plus would be to login using the IDP portal to ghostwrAIter itself and use the same credentials to query BZ
- Add specific packages picking them from IBS repos
- A plus would be to login using the IDP portal to ghostwrAIter itself and use the same credentials to query IBS
- A plus would be to desume the packages of interest and the right channel and version to be picked from the added BZ cases
AI for product management by a_jaeger
Description
Learn about AI and how it can help myself
What are the jobs that a PM does where AI can help - and how?
Goals
- Investigate how AI can help with different tasks
- Check out different AI tools, which one is best for which job
- Summarize learning
Resources
- Reading some blog posts by PMs that looked into it
- Popular and less popular AI tools
Work is done SUSE internally at https://confluence.suse.com/display/~a_jaeger/Hackweek+25+-+AI+for+a+PM and subpages.
Run local LLMs with Ollama and explore possible integrations with Uyuni by PSuarezHernandez
Description
Using Ollama you can easily run different LLM models in your local computer. This project is about exploring Ollama, testing different LLMs and try to fine tune them. Also, explore potential ways of integration with Uyuni.
Goals
- Explore Ollama
- Test different models
- Fine tuning
- Explore possible integration in Uyuni
Resources
- https://ollama.com/
- https://huggingface.co/
- https://apeatling.com/articles/part-2-building-your-training-data-for-fine-tuning/
FamilyTrip Planner: A Personalized Travel Planning Platform for Families by pherranz
Description
FamilyTrip Planner is an innovative travel planning application designed to optimize travel experiences for families with children. By integrating APIs for flights, accommodations, and local activities, the app generates complete itineraries tailored to each family’s unique interests and needs. Recommendations are based on customizable parameters such as destination, trip duration, children’s ages, and personal preferences. FamilyTrip Planner not only simplifies the travel planning process but also offers a comprehensive, personalized experience for families.
Goals
This project aims to: - Create a user-friendly platform that assists families in planning complete trips, from flight and accommodation options to recommended family-friendly activities. - Provide intelligent, personalized travel itineraries using artificial intelligence to enhance travel enjoyment and minimize time and cost. - Serve as an educational project for exploring Go programming and artificial intelligence, with the goal of building proficiency in both.
Resources
To develop FamilyTrip Planner, the project will leverage: - APIs such as Skyscanner, Google Places, and TripAdvisor to source real-time information on flights, accommodations, and activities. - Go programming language to manage data integration, API connections, and backend development. - Basic machine learning libraries to implement AI-driven itinerary suggestions tailored to family needs and preferences.
Explore simple and distro indipendent declarative Linux starting on Tumbleweed or Arch Linux by janvhs
Description
Inspired by mkosi the idea is to experiment with a declarative approach of defining Linux systems. A lot of tools already make it possible to manage the systems infrastructure by using description files, rather than manual invocation. An example for this are systemd presets for managing enabled services or the /etc/fstab file for describing how partitions should be mounted.
If we would take inspiration from openSUSE MicroOS and their handling of the /etc/ directory, we could theoretically use systemd-sysupdate to swap out the /usr/ partition and create an A/B boot scheme, where the /usr/ partition is always freshly built according to a central system description. In the best case it would be possible to still utilise snapshots, but an A/B root scheme would be sufficient for the beginning. This way you could get the benefit of NixOS's declarative system definition, but still use the distros package repositories and don't have to deal with the overhead of Flakes or the Nix language.
Goals
- A simple and understandable system
- Check fitness of
mkosior write a simple extensible image builder tool for it - Create a declarative system specification
- Create a system with swappable
/usr/partition - Create an A/B root scheme
- Swap to the new system without reboot (kexec?)
Resources
- Ideas that have been floating around in my head for a while
- https://0pointer.net/blog/fitting-everything-together.html
- GNOME OS
- MicroOS
- systemd mkosi
- Vanilla OS