![]() Testing the performance of your Java application is an artform rather than engineering and is a minefield of misconceptions, misunderstanding, and misinformation. We’ll talk about the various stages on the pipeline, about Kubeflow, KServe, Seldon, RaptorML and other ML k8s buzzwords a sneak peek into the future (which is actually… quite present).Įvery so often, you’ll read a performance benchmark (of a Java or other application), with bold claims for how well X performs compared to Y. In this talk, Almog, a Kubernetes maintainer, will review with us the various trends and open-source tools to build your own Production-ready AI Infrastructure stack. That being said, if you use a modern cloud-native stack, you can build your own AI Infrastructure stack fairly smoothly on Kubernetes. ![]() Productizing ML is hard, and many companies are starting to build their own infrastructure for “operationalized AI” and kinda re-inventing the wheel. Productizing ML with Kubernetes – Almog Baku We will also explore the AWS Controllers for Kubernetes (ACK), which lets you define your application’s AWS managed service resources using the familiar Kubernetes API and manifests! No need to use a different configuration system or log into the AWS Console! Come learn about the design of the AWS Controllers for Kubernetes, what features this new project provides, and the roadmap for service integration over the coming months. Currently, he is leading the NSF-funded BirdFlow project, which created the first predictive model capable of accurately forecasting the flight patterns of migratory birds.Join us at the JFrog TLV Swamp for the CNCF Meetup!ĭo you love the Kubernetes API and user experience? Do you love declaratively defining your application as a Deployment or Daemonset, a Service, and maybe an Ingress manifest, and letting the magic of Kubernetes handle the orchestration of your application deployment? We do too! Until now, if you had a Kubernetes application with some dependencies on an AWS resources (like S3 Bucket, SNS Topic, DynamoDB Table, etc) you needed to use another tool in addition to Kubernetes, like Terraform or CloudFormation, to manage the creation and lifecycle of those resource dependencies.Ĭan we do better? Kubernetes API is extensible and can be extended to manage even non-kubernetes resources! In this talk we will introduce this concept, of managing cloud resources through Kubernetes API, and discuss how this concept affects current software development and deployment practices. Sheldon's research focuses on #machinelearning and applied algorithms with applications in large-scale environmental data and dynamic ecological processes. “It's clear in the way he runs his courses that he cares deeply about students, and he constantly evaluates whether his teaching is effective in helping each student meet their goals,” said one student. ![]() He teaches in a creative and intuitive way that is impactful, enjoyable, and stress-free for the students.” ![]() Ramesh Sitaraman, distinguished professor and associate dean for educational programs and teaching, commended Sheldon's work, stating, “Dan is an extremely effective teacher who has consistently contributed in major ways to the teaching mission of the college. ![]() Machine learning continues to change how we interact with the world around us and one #UMassAmherst professor is going above and beyond in the field.ĭan Sheldon, associate professor at the Manning College of Information and Computer Sciences, UMass Amherst has been selected to receive the college's 2023 Outstanding Teaching Award, an honor given annually to a faculty member who demonstrates excellence and creativity in teaching and who has a positive impact on their students and mastery of their subject. ![]()
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