The IBM Presentation Template

CLAIMED – Component Library for AI, Machine Learning, ETL and Data Science A Kubeflow low code architecture A scalable open source (Trusted AI) omputer !ision pipeline for health care " #omeo Kienzler %ata Scientist IB' Ivan )esic ML *ngineer +ni(ersity ,ospital &asel Requirements - Rapid prototyping using visual editing and notebooks - Seamless scaling during development and deployment - GPU support - ML tools: PyData stack, TensorFlow, PyTorch, … - life science tools: DICOM input, DICOM output, … - Reproducibility - Data lineage - Reference implementation in open source - Collaboration support IB' Thin- 2021 / 2 2021 IB' orporation 3 Cloud annotations provides... Browser based image labeling: Classification / Object recognition training data IB' Thin- 2021 / 2 2021 IB' orporation ...in Open Source4 Kubeflow provides... Some prominent users: Amazon Web Services Auto "# IB Watson Services IB !s top clients $eplo%ment# &eproducibilit% 'otebooks# Pipelines# Serving# *raining# Scale ...on top of Kubernetes IB' Thin- 2021 / 2 2021 IB' orporation 00 Elyra provides... Some prominent users: *+ree IB clients# one ,ortune 500 compan% No Code / "ow Code " Pipeline $esign &e-usable pipeline components Interchangeability of Engines 23ubeflow, Air0ow# 4445 IB' Thin- 2021 / 2 2021 IB' orporation ...on top of JupyerLab, VSCode, ... 0. CLAIMED... Current users: Universit% 7ospital Basel Component Library for AI, otion4AI Machine Learning, ETL and Data Science )ortabilit% 'o Code / "ow Code )ipeline Components 8upyter Notebooks Sample )ipelines IB' Thin- 2021 / 2 2021 IB' orporation ...on top of Elyra and Kubeflow 05 Example Pipeline Components Category: Training Group: Distributed Name: TFJob The TFJob operator supports parallel training on multiple nodes and GPUs Example Pipeline Components Category: Processing Group: Distributed Name: SparkJob The SparkJob operator supports parallel processing on multiple nodes Example Pipeline Components Category: Tuning Group: Hyperopt Name: Katib Visualization of a hyper parameter optimization result Example Pipeline Components Category: Metric Group: Explainability Name: AIX360/LIME Example on how LIME helps to identify classification relevant areas of an image Example Pipeline Components Category: Metric Group: Adversarial Robustness Name: ART Example on how Adversarial Attacks happen Example Pipeline Components Category: Metric Group: AI Fairness Name: AIF360 Example on how the AIF360 toolkit computes fairness metrics and mitigates bias IBM 6atson 7penScale IB' 6atson OpenScale uses the same 7pen Source components on top of Kubernetes, Kubeflow and K8Ser(ing $id you know9 'odel 'onitoring and visualization of bias ./ IB' Thin- 2021 / 2 2021 IB' orporation (IB' Watson 7penScale) Component creation and cataloging... IB' Thin- 2021 / 2 2021 IB' orporation ...using Elyra and Data Science Asset Repository .0 Component consumption... IB' Thin- 2021 / 2 2021 IB' orporation ...using Elyra or Watson Orchestrator .. The final pipeline in Elyra.. IB' Thin- 2021 / 2 2021 IB' orporation …and KubeFlow .5 Summary - Rapid prototyping using visual editing and notebooks - Seamless scaling during development and deployment - GPU support - ML tools: PyData stack, TensorFlow, PyTorch, … - life science tools: DICOM input, DICOM output, … - Reproducibility - Data lineage - Reference implementation in open source - Collaboration support IB' Thin- 2021 / 2 2021 IB' orporation .9.

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Running ML/DL Workloads Using Red Hat Openshift Container Platform V3.11 Accelerate Your ML/DL Projects Platform Using Kubeflow and NVIDIA Gpus
Running ML/DL Workloads Using Red Hat OpenShift Container Platform v3.11 Accelerate your ML/DL Projects Platform using Kubeflow and NVIDIA GPUs January 2021 H17913.2 White Paper Abstract This white paper describes how to deploy Kubeflow v0.5 on Red Hat OpenShift Container Platform and provides recommendations for achieving optimal performance for ML/DL workloads using the latest NVIDIA Tesla GPUs. Dell Technologies Solutions Copyright The information in this publication is provided as is. Dell Inc. makes no representations or warranties of any kind with respect to the information in this publication, and specifically disclaims implied warranties of merchantability or fitness for a particular purpose. Use, copying, and distribution of any software described in this publication requires an applicable software license. Copyright © 2021 Dell Inc. or its subsidiaries. All Rights Reserved. Dell Technologies, Dell, EMC, Dell EMC and other trademarks are trademarks of Dell Inc. or its subsidiaries. Intel, the Intel logo, the Intel Inside logo and Xeon are trademarks of Intel Corporation in the U.S. and/or other countries. Other trademarks may be trademarks of their respective owners. Published in the USA 01/21 White Paper H17913.2. Dell Inc. believes the information in this document is accurate as of its publication date. The information is subject to change without notice. 2 Running ML/DL Workloads Using Red Hat OpenShift Container Platform v3.11 Accelerate your ML/DL Projects Platform using Kubeflow and NVIDIA GPUs White Paper Contents Contents Executive summary ........................................................................................................................ 4 Solution architecture ...................................................................................................................... 6 GPU accelerated TensorFlow training using TFJobs .................................................................. 9 Installing the GPU device plug-in ...............................................................................................
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Application Development with Azure
Application Development with Azure Karim Vaes Specialist – Azure Application Development 0032 497 219577 @kvaes Agenda • Kubernetes Kubernetes Kubernetes momentum “By 2020, more than 50% of enterprises Larger companies will run mission-critical, containerized are leading the cloud-native applications in production.” adoption. 77% For the organizations running Kubernetes today, 77%1 of those with more than 1,000 developers are running it in production. 1Heptio: state of Kubernetes 2018 What’s behind the growth? Kubernetes: the leading orchestrator shaping the future app development and management It’s widely used It’s vendor-neutral It’s community-supported Kubernetes is in production for A variety of cloud providers There’s a huge community of active global companies across industries1 offer robust Kubernetes support contributors supporting Kubernetes3 24,000 1.1 million contributors contributions since 2016 since 2016 1Kubernetes.io. “Kubernetes User Case Studies.” 2CNCF. “Kubernetes Is First…” 3CNCF. Keynote address. Azure Kubernetes Service (AKS) Ship faster, operate easily, and scale confidently with managed Kubernetes on Azure Manage Kubernetes Accelerate Build on an Run anything, with ease containerized enterprise-grade, anywhere development secure foundation Top scenarios for Kubernetes on Azure Lift and shift Machine Microservices IoT Secure DevOps to containers learning Cost saving Agility Performance Portability Automation without refactoring Faster application Low latency Build once, Deliver code faster and your app development processing run anywhere securely at scale Azure Kubernetes momentum Trusted by thousands of customers 30x Azure Kubernetes Service usage grew 30x since it was made generally available in June 2018 Dated November 2018 How Kubernetes works Kubernetes control Worker node Internet kubelet kube-proxy 1.
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Running Large-Scale Machine Learning Experiments in the Cloud
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Chapter 1 - Overview
Reference architecture Chapter 1 - Overview This document provides a complete reference architecture guide for infrastructure required for machine learning use cases. This document specifically covers open source machine learning software that includes Kubeflow using Charmed Kubernetes solution on Dell EMC hardware delivered by Canonical. This includes Dell EMC PowerEdge servers for workloads and storage based on Intel® Xeon® Scalable Processors and Dell EMC Networking. This guide discusses the Dell EMC hardware specifications and the tools and services to set up both the hardware and software, including the foundation cluster and the Kubernetes cluster to run machine learning workloads. It also covers other tools used for the monitoring and management of the cluster in detail and how all these components work together in the system. The guide also provides the deployment steps and references to configuration and automation scripts developed by Dell EMC and Canonical for the deployment process. Document ID Revisions Revisions Date Description March 2020 Initial release Acknowledgements This paper was produced by the following: Author: Andrey Grebennikov Support: Other: The information in this publication is provided “as is.” Dell Inc. makes no representations or warranties of any kind with respect to the information in this publication, and specifically disclaims implied warranties of merchantability or fitness for a particular purpose. Use, copying, and distribution of any software described in this publication requires an applicable software license. Copyright © Dell Inc. or its subsidiaries. All Rights Reserved. Dell, EMC, Dell EMC and other trademarks are trademarks of Dell Inc. or its subsidiaries. Other trademarks may be trademarks of their respective owners.
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Tensorflow 2.0 and Kubeflow for Scalable and Reproducable Enterprise Ai
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CNCF Webinar Taming Your AI/ML Workloads with Kubeflow
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Kubeflow: End to End ML Platform Animesh Singh
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Machine Learning at Scale with Kubernetes Aug 23Rd, 2018
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Application Development with Azure
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