Technical talks at universities by agamez

Description

This project aims to empower the next generation of tech professionals by offering hands-on workshops on containerization and Kubernetes, with a strong focus on open-source technologies. By providing practical experience with these cutting-edge tools and fostering a deep understanding of open-source principles, we aim to bridge the gap between academia and industry.

For now, the scope is limited to Spanish universities, since we already have the contacts and have started some conversations.

Goals

• Technical Skill Development: equip students with the fundamental knowledge and skills to build, deploy, and manage containerized applications using open-source tools like Kubernetes.
• Open-Source Mindset: foster a passion for open-source software, encouraging students to contribute to open-source projects and collaborate with the global developer community.
• Career Readiness: prepare students for industry-relevant roles by exposing them to real-world use cases, best practices, and open-source in companies.

Resources

• Instructors: experienced open-source professionals with deep knowledge of containerization and Kubernetes.
• SUSE Expertise: leverage SUSE's expertise in open-source technologies to provide insights into industry trends and best practices.

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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

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OpenPlatform Self-Service Portal by tmuntan1

Description

In SUSE IT, we developed an internal developer platform for our engineers using SUSE technologies such as RKE2, SUSE Virtualization, and Rancher. While it works well for our existing users, the onboarding process could be better.

To improve our customer experience, I would like to build a self-service portal to make it easy for people to accomplish common actions. To get started, I would have the portal create Jira SD tickets for our customers to have better information in our tickets, but eventually I want to add automation to reduce our workload.

Goals

• Build a frontend website (Angular) that helps customers create Jira SD tickets.
• Build a backend (Rust with Axum) for the backend, which would do all the hard work for the frontend.

Resources (SUSE VPN only)

• development site: https://ui-dev.openplatform.suse.com/login?returnUrl=%2Fopenplatform%2Fforms
• https://gitlab.suse.de/itpe/core/open-platform/op-portal/backend
• https://gitlab.suse.de/itpe/core/open-platform/op-portal/frontend

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Exploring Modern AI Trends and Kubernetes-Based AI Infrastructure by jluo

Description

Build a solid understanding of the current landscape of Artificial Intelligence and how modern cloud-native technologies—especially Kubernetes—support AI workloads.

Goals

Use Gemini Learning Mode to guide the exploration, surface relevant concepts, and structure the learning journey:

• Gain insight into the latest AI trends, tools, and architectural concepts.
• Understand how Kubernetes and related cloud-native technologies are used in the AI ecosystem (model training, deployment, orchestration, MLOps).

Resources

• Red Hat AI Topic Articles

  • https://www.redhat.com/en/topics/ai

• Kubeflow Documentation

  • https://www.kubeflow.org/docs/

• Q4 2025 CNCF Technology Landscape Radar report:

  • https://www.cncf.io/announcements/2025/11/11/cncf-and-slashdata-report-finds-leading-ai-tools-gaining-adoption-in-cloud-native-ecosystems/
  • https://www.cncf.io/wp-content/uploads/2025/11/cncfreporttechradar_111025a.pdf

• Agent-to-Agent (A2A) Protocol

  • https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/

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Rancher/k8s Trouble-Maker by tonyhansen

Project Description

When studying for my RHCSA, I found trouble-maker, which is a program that breaks a Linux OS and requires you to fix it. I want to create something similar for Rancher/k8s that can allow for troubleshooting an unknown environment.

Goals for Hackweek 25

• Update to modern Rancher and verify that existing tests still work
• Change testing logic to populate secrets instead of requiring a secondary script
• Add new tests

Goals for Hackweek 24 (Complete)

• Create a basic framework for creating Rancher/k8s cluster lab environments as needed for the Break/Fix
• Create at least 5 modules that can be applied to the cluster and require troubleshooting

Resources

• https://github.com/celidon/rancher-troublemaker
• https://github.com/rancher/terraform-provider-rancher2
• https://github.com/rancher/tf-rancher-up
• https://github.com/rancher/quickstart

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