3 Nov 2021
Don’t miss out! Join us at our next event: KubeCon + CloudNativeCon Europe 2022 in Valencia, Spain from May 17-20. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
containerd Introduction and Deep Dive - Phil Estes, Amazon; Maksym Pavlenko & Derek McGowan, Apple; Mike Brown, IBM
Join containerd maintainers for an introduction and deep dive into the latest updates on containerd. This last year has seen tremendous growth in both project usage and contribution. From end user CLI to low level runtime implementations, there have been exciting developments and proposals toward making containerd more stable and shaping the next generation of container use cases. The maintainers will go over internal changes to containerd which help make the core project interfaces cleaner and easier to integrate with from different components and plugins. For Kubernetes use cases, we will cover related changes happening in containerd including updates in the CRI implementation. Finally, the maintainers will cover exciting new features and sub-projects such as nerdctl, lazy-pulling (stargz), shim plugability, and more.
containerd Introduction and Deep Dive - Phil Estes, Amazon; Maksym Pavlenko & Derek McGowan, Apple; Mike Brown, IBM
Join containerd maintainers for an introduction and deep dive into the latest updates on containerd. This last year has seen tremendous growth in both project usage and contribution. From end user CLI to low level runtime implementations, there have been exciting developments and proposals toward making containerd more stable and shaping the next generation of container use cases. The maintainers will go over internal changes to containerd which help make the core project interfaces cleaner and easier to integrate with from different components and plugins. For Kubernetes use cases, we will cover related changes happening in containerd including updates in the CRI implementation. Finally, the maintainers will cover exciting new features and sub-projects such as nerdctl, lazy-pulling (stargz), shim plugability, and more.
- 4 participants
- 31 minutes
30 Oct 2021
Don’t miss out! Join us at our next event: KubeCon + CloudNativeCon Europe 2022 in Valencia, Spain from May 17-20. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
A Better and More Efficient ML Experience for CERN Users - Ricardo Rocha & Dejan Golubovic, CERN
Experiments at CERN such as the Large Hadron Collider (LHC) generate petabytes of new data every year, to be stored and analyzed by thousands of physicists around the world. In just a couple years, an upgrade to the LHC will trigger a 10x increase in the amount of data posing a challenge to the existing infrastructure. This session covers how machine learning has been gaining momentum in the high energy physics (HEP) community and particularly at CERN, as a viable option to handle the data growth with a similar amount of resources. The focus is on one particular service based on Kubeflow, and how we extend the existing functionality to offer our users a familiar and seamless integration with site services. How centralizing resources has improved our overall resource usage, how we extended existing functionality to manage end user tokens and credentials allowing access to on-premises storage, and how we explore tools like Harbor, Trivy, OPA and Falco to ensure a reproducible and secure flow from interactive analysis, to model training and finally serving.
A Better and More Efficient ML Experience for CERN Users - Ricardo Rocha & Dejan Golubovic, CERN
Experiments at CERN such as the Large Hadron Collider (LHC) generate petabytes of new data every year, to be stored and analyzed by thousands of physicists around the world. In just a couple years, an upgrade to the LHC will trigger a 10x increase in the amount of data posing a challenge to the existing infrastructure. This session covers how machine learning has been gaining momentum in the high energy physics (HEP) community and particularly at CERN, as a viable option to handle the data growth with a similar amount of resources. The focus is on one particular service based on Kubeflow, and how we extend the existing functionality to offer our users a familiar and seamless integration with site services. How centralizing resources has improved our overall resource usage, how we extended existing functionality to manage end user tokens and credentials allowing access to on-premises storage, and how we explore tools like Harbor, Trivy, OPA and Falco to ensure a reproducible and secure flow from interactive analysis, to model training and finally serving.
- 2 participants
- 24 minutes
30 Oct 2021
Don’t miss out! Join us at our next event: KubeCon + CloudNativeCon Europe 2022 in Valencia, Spain from May 17-20. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
Case Study: Developing and Scaling Kubeflow’s Web Apps - Andrey Velichkevich, Cisco & Kimonas Sotirchos, Arrikto
At this moment, Kubeflow maintains at least 5 different web apps, for managing Notebooks, PVCs, Tensorboards, Models, AutoML Experiments, that allow users to interact with the platform. At their core these web apps act as a graphical interface for performing CRUD operations on top of K8s Objects and Custom Resources. Designing, creating, and maintaining these apps is not a trivial task. In this talk, attendees will learn how the Kubeflow community overcame all the challenges to create true cloud native web apps, for managing ML workflows on top of K8s. Follow our journey as we explore the architectural decisions we made regarding authentication with Istio and authorization with K8s SubjectAccessReviews. How we factored out the common code and enabled application scalability. The UX decisions for managing K8s objects via a GUI. And last but not least, how we can efficiently fetch new data for Kubeflow dashboard in the context of how users can perform advanced AutoML techniques.
Case Study: Developing and Scaling Kubeflow’s Web Apps - Andrey Velichkevich, Cisco & Kimonas Sotirchos, Arrikto
At this moment, Kubeflow maintains at least 5 different web apps, for managing Notebooks, PVCs, Tensorboards, Models, AutoML Experiments, that allow users to interact with the platform. At their core these web apps act as a graphical interface for performing CRUD operations on top of K8s Objects and Custom Resources. Designing, creating, and maintaining these apps is not a trivial task. In this talk, attendees will learn how the Kubeflow community overcame all the challenges to create true cloud native web apps, for managing ML workflows on top of K8s. Follow our journey as we explore the architectural decisions we made regarding authentication with Istio and authorization with K8s SubjectAccessReviews. How we factored out the common code and enabled application scalability. The UX decisions for managing K8s objects via a GUI. And last but not least, how we can efficiently fetch new data for Kubeflow dashboard in the context of how users can perform advanced AutoML techniques.
- 2 participants
- 20 minutes
30 Oct 2021
Don’t miss out! Join us at our next event: KubeCon + CloudNativeCon Europe 2022 in Valencia, Spain from May 17-20. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
Defending Against Adversarial Model Attacks Using Kubeflow - Animesh Singh & Andrew Butler, IBM
The application of AI algorithms in domains such as self-driving cars, facial recognition, and hiring holds great promise. At the same time, it raises legitimate concerns about AI algorithms robustness against adversarial attacks. Widespread adoption of AI algorithms where the predictions are hidden or obscured from the trained eye of the subject expert, opportunities for a malicious actor to take advantage of the AI algorithms grow considerably, necessitating the addition of adversarial robustness training and checking. To protect against and mitigate the damages caused by these malicious actors, this talk will examine how to build a pipeline that’s robust against adversarial attacks by leveraging Kubeflow Pipelines and integration with LFAI Adversarial Robustness Toolbox (ART). Additionally we will show how to test a machine learning model's adversarial robustness in production on Kubeflow Serving, by virtue of Payload logging (KNative eventing) and ART.
Defending Against Adversarial Model Attacks Using Kubeflow - Animesh Singh & Andrew Butler, IBM
The application of AI algorithms in domains such as self-driving cars, facial recognition, and hiring holds great promise. At the same time, it raises legitimate concerns about AI algorithms robustness against adversarial attacks. Widespread adoption of AI algorithms where the predictions are hidden or obscured from the trained eye of the subject expert, opportunities for a malicious actor to take advantage of the AI algorithms grow considerably, necessitating the addition of adversarial robustness training and checking. To protect against and mitigate the damages caused by these malicious actors, this talk will examine how to build a pipeline that’s robust against adversarial attacks by leveraging Kubeflow Pipelines and integration with LFAI Adversarial Robustness Toolbox (ART). Additionally we will show how to test a machine learning model's adversarial robustness in production on Kubeflow Serving, by virtue of Payload logging (KNative eventing) and ART.
- 2 participants
- 21 minutes
30 Oct 2021
Don’t miss out! Join us at our next event: KubeCon + CloudNativeCon Europe 2022 in Valencia, Spain from May 17-20. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
Making Complex R Forecast Applications Into Production Using Argo Workflow - Natalia Costa Araujo, Pedro Szloma Herr Zaterka & Matheus Sesso Gay, 4intelligence
R has tools to help multidisciplinary teams succeed. Usually, the processes developed in R are created for problems restricted to academics researchers. As more companies search for data and scientific methods to guide business decisions, creating a scalable R environment will be a critical step towards success.
Making Complex R Forecast Applications Into Production Using Argo Workflow - Natalia Costa Araujo, Pedro Szloma Herr Zaterka & Matheus Sesso Gay, 4intelligence
R has tools to help multidisciplinary teams succeed. Usually, the processes developed in R are created for problems restricted to academics researchers. As more companies search for data and scientific methods to guide business decisions, creating a scalable R environment will be a critical step towards success.
- 2 participants
- 15 minutes
30 Oct 2021
Don’t miss out! Join us at our next event: KubeCon + CloudNativeCon Europe 2022 in Valencia, Spain from May 17-20. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
Security Best Practices for AI on Kubernetes - Guy Salton, Run:AI
Data Scientists and MLOps engineers are embracing containers and Kubernetes for building, debugging, training and deploying deep learning models. There are many advantages for using Kubernetes for AI workloads, but is it secure? In this talk, we will present the security concerns for AI workloads running on Kubernetes and how to mitigate them: Which user is used inside the container? Can the Data Scientist use privileged escalation from his container and access the host filesystem? How to allow Data Scientists to install python packages in a secure manner? Can a Data Scientist have access other researchers code and data from his container? Guy Salton, Solution Engineering Lead at Run:AI, will cover all the concerns above, and provide security best practices to MLOps engineers, to make the everyday work of Data Scientists both secure and productive.
Security Best Practices for AI on Kubernetes - Guy Salton, Run:AI
Data Scientists and MLOps engineers are embracing containers and Kubernetes for building, debugging, training and deploying deep learning models. There are many advantages for using Kubernetes for AI workloads, but is it secure? In this talk, we will present the security concerns for AI workloads running on Kubernetes and how to mitigate them: Which user is used inside the container? Can the Data Scientist use privileged escalation from his container and access the host filesystem? How to allow Data Scientists to install python packages in a secure manner? Can a Data Scientist have access other researchers code and data from his container? Guy Salton, Solution Engineering Lead at Run:AI, will cover all the concerns above, and provide security best practices to MLOps engineers, to make the everyday work of Data Scientists both secure and productive.
- 1 participant
- 21 minutes
30 Oct 2021
Don’t miss out! Join us at our next event: KubeCon + CloudNativeCon Europe 2022 in Valencia, Spain from May 17-20. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
Serving Machine Learning Models at Scale Using KServe - Yuzhui Liu, Bloomberg
KServe (previously known as KFServing) is a serverless open source solution to serve machine learning models. With machine learning becoming more widely adopted in organizations, the trend is to deploy larger numbers of models. Plus, there is an increasing need to serve models using GPUs. As GPUs are expensive, engineers are seeking ways to serve multiple models with one GPU. The KServe community designed a Multi-Model Serving solution to scale the number of models that can be served in a Kubernetes cluster. By sharing the serving container that is enabled to host multiple models, Multi-Model Serving addresses three limitations that the current ‘one model, one service’ paradigm encounters: 1) Compute resources (including the cost for public cloud), 2) Maximum number of pods, 3) Maximum number of IP addresses. 4) Maximum number of services This talk will present the design of Multi-Model Serving, describe how to use it to serve models for different frameworks, and share benchmark stats that demonstrate its scalability.
Serving Machine Learning Models at Scale Using KServe - Yuzhui Liu, Bloomberg
KServe (previously known as KFServing) is a serverless open source solution to serve machine learning models. With machine learning becoming more widely adopted in organizations, the trend is to deploy larger numbers of models. Plus, there is an increasing need to serve models using GPUs. As GPUs are expensive, engineers are seeking ways to serve multiple models with one GPU. The KServe community designed a Multi-Model Serving solution to scale the number of models that can be served in a Kubernetes cluster. By sharing the serving container that is enabled to host multiple models, Multi-Model Serving addresses three limitations that the current ‘one model, one service’ paradigm encounters: 1) Compute resources (including the cost for public cloud), 2) Maximum number of pods, 3) Maximum number of IP addresses. 4) Maximum number of services This talk will present the design of Multi-Model Serving, describe how to use it to serve models for different frameworks, and share benchmark stats that demonstrate its scalability.
- 4 participants
- 31 minutes
30 Oct 2021
Don’t miss out! Join us at our next event: KubeCon + CloudNativeCon Europe 2022 in Valencia, Spain from May 17-20. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
Sponsored Keynote: Evolving with Kubernetes: Embracing Model Ops - Steven Huels, Sr Director, AI Cloud Services, Red Hat
Kubernetes changed the way in which applications are developed. AI and Machine Learning has changed what is required of an application which has driven changes in the way we put intelligent applications into production. Applying the DevOps principles to AI and Machine Learning outputs or Model Ops, can help you get your models from pilot to production safely, securely, and with repeatability.
Sponsored Keynote: Evolving with Kubernetes: Embracing Model Ops - Steven Huels, Sr Director, AI Cloud Services, Red Hat
Kubernetes changed the way in which applications are developed. AI and Machine Learning has changed what is required of an application which has driven changes in the way we put intelligent applications into production. Applying the DevOps principles to AI and Machine Learning outputs or Model Ops, can help you get your models from pilot to production safely, securely, and with repeatability.
- 1 participant
- 6 minutes
30 Oct 2021
Don’t miss out! Join us at our next event: KubeCon + CloudNativeCon Europe 2022 in Valencia, Spain from May 17-20. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
Sponsored Keynote: Kubeflow Pipelines v2: The Next Generation of MLOps on Kubernetes - Karthik Ramachandran, Product Manager, Google Cloud Vertex AI, Google
We would like to introduce Kubeflow Pipelines v2, which makes it significantly easier to author and manage model training Pipelines. In this talk, we’ll review the new capabilities of the project, talk a little bit about our plans for the future, and discuss where and how the community can help.
Sponsored Keynote: Kubeflow Pipelines v2: The Next Generation of MLOps on Kubernetes - Karthik Ramachandran, Product Manager, Google Cloud Vertex AI, Google
We would like to introduce Kubeflow Pipelines v2, which makes it significantly easier to author and manage model training Pipelines. In this talk, we’ll review the new capabilities of the project, talk a little bit about our plans for the future, and discuss where and how the community can help.
- 1 participant
- 9 minutes
30 Oct 2021
Don’t miss out! Join us at our next event: KubeCon + CloudNativeCon Europe 2022 in Valencia, Spain from May 17-20. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
What, Why and How of Federated Machine Learning – Implementation Using FATE, KubeFATE, and FATE-Operator for Kubeflow - Neeraj Arora & Layne Peng, VMware
Federated Machine Learning (FML) is an emerging technology that makes it possible to extract insights from widely dispersed data sources. It may also be used with privacy enhancing measures to reveal insights without revealing the underlying data. In doing so, it gives global organizations an opportunity to utilize worldwide data while adhering to regulations for different geographic regions, allows insights to be developed without moving large amounts of data to a central location, and in a multi-party scenario allows each party to control their individual decision to participate. This talk will introduce FML discussing its basic principles, its manifestations, high-level use-cases, and tie these in with the technology being developed. To move from theory to practice, we will demonstrate how FML can be implemented on Kubernetes using FATE, KubeFATE, and integrated with Kubeflow using the FATE-Operator. We’ll also discuss new features being implemented into FATE for realistic use-cases. A similar presentation was delivered at CNOS Virtual Summit China in July 2020 and focused on the integration of FATE into Kubeflow. The current presentation will cover technical ground instead on the challenges and opportunities of FML for real-world use-cases encountered since.
What, Why and How of Federated Machine Learning – Implementation Using FATE, KubeFATE, and FATE-Operator for Kubeflow - Neeraj Arora & Layne Peng, VMware
Federated Machine Learning (FML) is an emerging technology that makes it possible to extract insights from widely dispersed data sources. It may also be used with privacy enhancing measures to reveal insights without revealing the underlying data. In doing so, it gives global organizations an opportunity to utilize worldwide data while adhering to regulations for different geographic regions, allows insights to be developed without moving large amounts of data to a central location, and in a multi-party scenario allows each party to control their individual decision to participate. This talk will introduce FML discussing its basic principles, its manifestations, high-level use-cases, and tie these in with the technology being developed. To move from theory to practice, we will demonstrate how FML can be implemented on Kubernetes using FATE, KubeFATE, and integrated with Kubeflow using the FATE-Operator. We’ll also discuss new features being implemented into FATE for realistic use-cases. A similar presentation was delivered at CNOS Virtual Summit China in July 2020 and focused on the integration of FATE into Kubeflow. The current presentation will cover technical ground instead on the challenges and opportunities of FML for real-world use-cases encountered since.
- 2 participants
- 21 minutes
