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Servers/Back End: Kubeflow, Kubernetes and EdgeX Foundry

Submitted by Roy Schestowitz on Wednesday 9th of October 2019 03:11:27 AM Filed under
Server
  • Designing an open source machine learning platform for autonomous vehicles

    Self-driving cars are one of the most notable technology breakthroughs of recent years. The progress that has been made from the DARPA challenges in the early 2000s to Waymo’s commercial tests is astounding. Despite this rapid progress, much still needs to be done to reach full autonomy without humans in the loop – an objective also referred to as SAE Level 5. Infrastructure is one of the gaps that need to be bridged to achieve full autonomy.

    Embedding the full compute power needed to fully automatise vehicles may prove challenging. On the other hand, relying on the cloud at scale would pose latency and bandwidth issues. Therefore, vehicle autonomy is a case for edge computing. But, how to distribute and orchestrate AI workloads, data storage, and networking at the edge for such a safety-critical application? We propose an open-source architecture that will address these questions.

    [...]

    In order to implement an open-source machine learning platform for autonomous vehicles, data scientists can use Kubeflow: the machine learning toolkit for Kubernetes. The Kubeflow project is dedicated to making deployments of machine learning workflows simple, portable and scalable. It consists of various open-source projects which can be integrated to work together. This includes Jupyter notebooks and the TensorFlow ecosystem. However, since the Kubeflow project is growing very fast, its support is soon going to expand over other open-source projects, such as PyTorch, MXNet, Chainer, and more.

    Kubeflow allows data scientists to utilize all base machine learning algorithms. This includes regression algorithms, pattern recognition algorithms, clustering and decision making algorithms. With Kubeflow data scientists can easily implement tasks which are essential for autonomous vehicles. These tasks include object detection, identification, recognition, classification, and localisation.

  • Kubernetes communication, SRE struggles, and more industry trends

    As part of my role as a senior product marketing manager at an enterprise software company with an open source development model, I publish a regular update about open source community, market, and industry trends for product marketers, managers, and other influencers. Here are five of my and their favorite articles from that update.

  • Introducing a Tech Preview of Containerized Ceph on Kubernetes

    We have been hard at work to bring a containerized version of Ceph to Kubernetes, and we are very excited to announce that we are releasing a technical preview of our project to run SUSE Enterprise Storage (powered by Ceph) on SUSE CaaS Platform (powered by Kubernetes). We leverage the most modern, powerful application management framework to make Ceph lifecycle management easier, and we provide an easy way for SUSE CaaS Platform users to get Kubernetes-native persistent storage for their Kubernetes cluster backed by enterprise-grade software-defined storage.

    [...]

    The good news is that work on Rook and Ceph-Rook integration is a concentrated effort upstream. There are many eyes—and many fingers—working to make Ceph better on Kubernetes. We at SUSE are in a good position to make sure that Ceph and Rook work upstream will meet the unique needs of our customers, and we are thrilled that our customers and their needs are able to make upstream better.

  • Making The IoT More Open: A Common Framework For IoT Edge Computing With EdgeX Foundry

    The internet of things (IoT) is a diverse space, but it’s also fragmented by design, whether it’s consumer IoT or industrial IoT. In 2015, Dell started working on a project called Project Fuse to weave together the diverse and fragmented world of IoT. The idea was to build the right architecture for IoT and edge computing.

    The team working on the project quickly realized that they needed to extend the cloud-native principles — things like microservice-based architectures and platform independence — as close as possible to the device edge so that there would be more flexibility in how solutions are devised. In order to succeed, the project needed to be vendor-neutral, interoperable and open.

»

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