 Welcome to our talk. This is Yuan from Accurity. I'm one of the maintainers of Argo workflows. I'm presenting today with my co-speaker, Nien, who's the Tech Lead Manager of Map Infrastructure at TwoSimple. Today, we are going to talk about how to automate map building pipelines for safe autonomous driving with Argo workflows at TwoSimple. With that, I'm going to hand over to Nien to talk about Argo workflows for autonomous driving at TwoSimple. Okay. Thank you, Yuan. Glad to be here. Today, I'm going to talk a little bit up about how we use Argo workflows in our map building process. Workflow engine is one of the fundamental tools in our map building process. When we compare the different workflow tools, we choose Argo workflows for these reasons. First, it's open source. It gives us the opportunity to read the source code and make necessary changes if needed. Second, it's Kubernetes native. All our services are hosted on Kubernetes, and it makes perfect sense to have a Kubernetes native workflow engine for easier management and maintenance. Three, availability and scalability. We value availability and scalability a lot, and that is what Argo workflow can bring us. Four or five, rich features. There are a lot of features needed in map building process, especially when human efforts are needed in the middle of a workflow. We need to pause the workflow and wait for the human input. In the collaboration and the support, we have received very good collaboration that support equity and the help us with some critical features. Next slide, please. Here, let me name some examples of collaborations we had. One example is during our process, sometimes before we finish the workflow, we don't know which step can cause issues or needs redo. If one step fails, we might need to retry a few steps before the failed one. If we can have a feature to retry an arbitrary node that will make it more flexible to implement. Another example is we listened on workflow events for notify other microservices, the status changes. We need event aggregation, customization to better meet the requirements. Both of these features are critical for us, and equity helped us a lot on these. We worked together on issues and the test. We had a lot of pipelines in our whole map building process like a single-back processing pipeline and the patch pipeline. On the other hand, we use Argo workflow, not only for the map building process, but also in our CICD system for application deployment and deployment pipeline. Here are some screenshots of our workflows and the data processing, intermediate results. As you can see in the picture, we have some complex workflows, and on the right side, we have some map building intermediate data. I'm going to hand it over to you again. Besides the specific use cases are too simple. That name mentioned previously, machine learning pipelines, CICD, and infrastructure automation, as well as data processing are among the popular use cases we found in the community members. For example, TripAdvisor uses Argo workflows for CICD automation of their machine learning models. Intuit uses it for distributed load testing. You can find more use cases and past presentations at various conferences and meetups from the community in the awesome Argo repository linked at the bottom. Next, I'd like to talk about community contributions. This is a latest diagram from CNCF that provides a project rankings for developer velocity based on project activities such as activities on prerequisites and issues, the number of commits, and so on. Argo is one of the fastest growing CNCF projects. Argo currently has contributions from over 800 contributors. We also provide mentoring for new contributors, as well as regular contributors meetings to provide an opportunity for the community to participate in design discussions or decisions. There are 40 core maintainers to all the Argo projects from over 10 organizations. Besides active contributions from the community, Argo is also widely adopted. It is used and trusted by more than 200 end user companies, more than 14k slack members, more than 25k GitHub stars, and 6k folks on GitHub. We also have active feedback loop from user surveys that we sent out every year, as well as in-app surveys that users feel out when using the Argo workflows UI. Below are some example screenshots of the in-app survey. We categorized use cases and actively collect additional use cases from our users. On GitHub, we have different issue templates for bug reports, enhancement requests, mentoring requests, and so on. We also open GitHub discussions for Q&As and showcases that are easily searchable on GitHub. There are different slack channels for different Argo sub-projects in the CNCF Slack org workspace, with over 14,000 members. Last but not least, as I mentioned earlier, we host regular contributors meetings to help new or existing contributors. That's a wrap. If you have any questions, you can find us on the CNCF Slack and on various social media. Thank you for listening and see you around at ArgoCon.