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Conference: OpenAI + Data Forum 2022
Authors: Neethu Elizabeth Simon, Scott Thomas
2022-06-23
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Converting an old-school textile inspection machine into a smart system using AI/ML is effective and affordable even in the commodity fabric manufacturing industry.
- Textile inspection is traditionally labor-intensive and error-prone.
- Computer vision-based AI/ML solution using open source tools was developed for textile defect detection during the fabric inspection process.
- Old-school manual fabric inspection machine was successfully integrated with cameras and open source AI/ML tools running on high-performance compute device.
- Reasonably priced system was affordably applied to a much lower cost labor-intensive industry without expensive retooling or excessively high-priced technology.
- Implementation and integration challenges encountered during design and development of this unique solution were resolved.
- Model worked but was not scalable enough and was sensitive to folds and creases.
- Inferencing was good but the system was not robust enough to handle high motor speed.
Conference: OpenAI + Data Forum 2022
Authors: Charles Adetiloye, Keith Mattix
2022-06-23
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Kubeflow Metal is a new way of deploying Kubeflow onto a Kubernetes cluster on bare metal servers, providing a low friction, high velocity way to deploy an ML platform in an easy, experimental on-prem environment.
- Kubeflow Metal is a terraform module that deploys Kubeflow on a Kubernetes cluster on bare metal servers
- It is a cheaper alternative to cloud infrastructure with a fixed cost
- It allows for quick bootstrapping of an ML environment or infrastructure for a team
- Deployment is elastic and easily scalable
- It can be used for plugging into a CI/CD process
- It is useful for cases where data cannot be moved to the cloud, such as financial or insurance data
- Kubeflow Metal is looking for people to help improve the project
Conference: KubeCon + CloudNativeCon Europe 2022
Authors: Abdullah Gharaibeh, Aldo Culquicondor, Alex Wang
2022-05-19
The Kubernetes Working Group Batch was newly formed in the beginning of 2022. The Working Group aims to be a forum to discuss and propose enhancements to support for Batch (eg. HPC, AI/ML, data analytics, CI) workloads in core Kubernetes. We want to unify the way users deploy batch workloads to improve portability and to simplify supportability for Kubernetes providers. In this session, you will learn about the WG goals and roadmap , as well as the early efforts performed by our contributors.Click here to view captioning/translation in the MeetingPlay platform!
Conference: KubeCon + CloudNativeCon Europe 2022
Authors: Madalina Lazar, Denisio Togashi
2022-05-18
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Telemetry Aware Scheduling is an open-source project that uses telemetry to make smarter scheduling decisions for workloads in Kubernetes clusters.
- Telemetry Aware Scheduling (TAS) is an open-source project that extends Kubernetes' scheduling paradigm to use knowledge of resources to impact scheduling decisions.
- TAS uses telemetry to help make scheduling decisions and is an extender of the Kubernetes scheduler.
- TAS allows for filtering and scoring nodes and utilizes node affinity rules via fixed and custom labels.
- TAS uses telemetry where scheduling policies that are structurally based on rules which are based on metrics that come from the cluster.
- TAS requires a metrics pipeline to expose, collect, store, and make metrics available to the Kubernetes custom metrics API.
- TAS works together with the default scheduler and returns a suggested outcome of pod placement to the default scheduler.
- TAS supports multi-metric rules that contain multiple metrics and can link them together with operators such as any off or all of.