Are you a community manager looking for insights into your community using open source tools? Are you a data scientist looking to build custom visualizations in Python or with the Dash framework? This workshop is for you! Red Hat’s Open Source Program Office’s data science team will walk you through how they dive into contributor and community data using Augur and 8Knot. This workshop requires Python proficiency. Participants have the choice to work on their local machine (installs needed below) or a supplied cloud workspace. If you plan on working in the cloud workspace, we would greatly appreciate it if you would email jkunstle@redhat.com so that we have a headcount.

Learn how CentOS works and how you can make it work better for you. CentOS is the open source, community operating system that releases just ahead of RHEL. The CentOS project also provides a space for people to build technologies on top of the operating system for specific use cases. This includes Special Interest Groups (SIGs) that package various types of software, including cloud computing, storage, hyerscale deployments, and automotive uses. We will present an overview of the CentOS ecosystem, showing how the operating system is developed, what the various SIGs are doing, and what other deliverables you can expect. We will then provide a packaging workshop where you can learn how to package your favorite software, whether you want it in Fedora, EPEL, a CentOS SIG, or just for your own personal use. No prior packaging experience is necessary, but we will assume a basic familiarity with Linux and the command line.

Ceph is an open source distributed object store, network block device, and file system designed for reliability, performance, and scalability. It runs on commodity hardware, has no single point of failure, and is supported in the Linux kernel. This tutorial will describe the Ceph architecture, share its design principles, and discuss how it can be part of a cost-effective, reliable cloud stack. The de-facto standard for OpenStack storage, Ceph is leading the rising tide of Software-defined-Storage and a top contender in the Kubernetes and S3 storage use cases.

H) Understanding the relationships between various resource types in Kubernetes is crucial to understanding Kubernetes in general. It's unintuitive and takes many queries to gather owner/dependent hierarchies and find indirect references. By encoding relationship semantics into a tool such as k8sviz, and then using the tool to visualize real Kubernetes resources, we can quickly understand and communicate about the overall state of a Kubernetes-based service. This session is suitable for those with some basic Kubernetes knowledge and those looking to teach others about Kubernetes, or about a service running on Kubernetes. We'll discuss how to use automated visualizations, look at some examples, and discuss ideas for improving automated generation of Kubernetes diagrams.

The data available about open source projects can feel like a tsunami with wave after wave of overwhelming data. But there are ways to make this more manageable by finding and focusing on the metrics that matter the most for you. One of the ways the open source CHAOSS project overcomes the data Tsunami is by collecting sets of metrics that are more useful together using techniques from data science. This session will highlight how people can use data science to generate meaningful insights about their open source software communities. This talk will start with a discussion about how to approach the tsunami of data using data science based approaches to move from data points toward insight and wisdom about your open source software. The first (and largest) step in a data science workflow is the data collection and preprocessing. By having access to data in a relational database populated by Augur, the major lifting is done and allows data scientists to focus on the analysis. The next section will discuss how collections of related metrics can be used to understand some aspect of your community more holistically than looking at individual metrics. The final portion of this talk will include examples of how to interpret the data from these collections of metrics to move beyond analysis and find tangible ways to make your open source community even better. We’ll show examples using the open source Augur and 8Knot toolchain to show what is possible with structured data, a python stack, and metrics informed by the metrics models from the CHAOSS project. The audience will walk away with tips and techniques for making sense of those waves of data using collections of metrics and data science to result in actionable insights about your open source software.

Replicating a development environment is tedious, but using Ansible and chezmoi makes your environment easy to replicate. I've customized my environment to my preferences over the past decade. I often pick up new tools or configurations on-the-job or at-home. Duplicating configuration changes between multiple systems sucked, until I started managing it with Ansible and chezmoi. This session is suitable for developers and sysadmins comfortable with the command line. Prior Ansible experience is unnecessary. We'll discuss practical strategies for organizing environment automation, experiences managing multiple types of packages with Ansible playbooks, and how to keep home and work setups consistent, but also separated.

Kevin Howell,

Principal Software Engineer, Red Hat

It's no lie that the Linux desktop has been changing greatly. Through technological and social shifts, the desktop and community we know today is a far cry from what it was ten years ago. Additionally, the users and contributors entering the space are younger, more diverse, and more open-minded than ever before. This panel is a discussion between a diverse set of stakeholders, from the big and old to the small and new. We’ll discuss pressing issues in the ecosystem, what our outlook and focus should be directed forwards, and much more. Our primary topics and goals are to discuss Application Ecosystem, Accessibility, Sustainability & Funding, Outreach & Growth, Distros, Standardization, and Community. We hope our panel can demonstrate an insider’s perspective to the exciting future of the Linux desktop, get outsiders (technical and non-technical) involved, discuss pressing issues between representatives of different communities, and share our thoughts on how we can make the year of the Linux desktop finally happen.

You've probably seen how to create a Function-as-a-Service with one of the cloud providers, but if this is all you know about Serverless, prepare to have your mind blown! In this session we'll show you how to create a production-grade, cloud-agnostic, event-driven serverless solution with Quarkus, a Java stack optimized for fast startup and small footprint; and Knative, an open source community project for deploying, running and managing serverless applications on Kubernetes. Say goodbye to vendor lock-in and hello to Supersonic Subatomic Java-based Serverless bliss! This session will feature lots of fun demos, code samples and interaction.

Kevin Dubois,

Principal Developer Advocate, Red Hat

While Generative AI and Large Language Models are gaining traction, old-school machine learning is still a powerful tool for uncovering hidden patterns in corporate data. This data can be a goldmine of information, revealing insights into customer behavior, market trends, and operational inefficiencies. By leveraging key machine learning techniques, businesses can gain a competitive edge by identifying new opportunities, improving decision-making, and streamlining operations. With predictive modeling, we can now quickly consume and analyze large datasets to uncover hidden patterns and trends. By using open source time series forecasting ML models like ARIMA and Prophet, we can provide more accurate predictions and insights in real-time, enabling organizations and teams to streamline processes and increase efficiency, improve and manage customer risk, and adapt to changing market conditions. In this talk we will discuss: Open Source tooling for building predictive ML models (Python, Jupyter, MLFLow) Time series forecasting techniques Tips for managing ML workflows and model interpretations Attendees will leave this talk with a deeper understanding of predictive ML models and how open source can empower us to be more data driven.