Yannis Zarkadas. Enterprise data science workflows on kubeflow

1. Enterprise Data Science Workflows on Kubeflow Use GitOps to deploy and manage your Kubeflow cluster. Perform an end-to-end data science workflow on Kubeflow. Stefano Fioravanzo Yannis Zarkadas Arrikto

2. Simplify. Accelerate. Collaborate. arrik.to/odsc20 GitOps and Multi-Tenancy Combined for an Enterprise Data Science Experience on Kubeflow Stefano Fioravanzo Yannis Zarkadas Software Engineer Software Engineer 2

3. Simplify. Accelerate. Collaborate. arrik.to/odsc20 ● How to deploy and manage Kubeflow in a GitOps manner ● How to make sure you run Kubeflow in a secure way ● How to optimize and build production-ready models faster Why is this important? ✓ Simplify deployment and management of Kubeflow ✓ Accelerate time to production ✓ Collaborate in a secure and isolated manner What You’ll Learn In This Session 3

4. Simplify. Accelerate. Collaborate. arrik.to/odsc20 What is Kubeflow The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes: simple, portable and scalable. 4

5. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Perception: ML Products are mostly about ML Credit: Hidden Technical Debt of Machine Learning Systems, D. Sculley, et al. Configuration Data Collection Data Verification Feature Extraction Process Management Tools Analysis Tools Machine Resource Management Serving Infrastructure Monitoring ML Code 5

6. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Reality: ML Requires DevOps; lots of it Configuration Data Collection Data Verification Feature Extraction Process Management Tools Analysis Tools Machine Resource Management Serving Infrastructure Monitoring ML Code Credit: Hidden Technical Debt of Machine Learning Systems, D. Sculley, et al. 6

7. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Kubeflow components 7 Jupyter Notebooks Workflow Building Pipelines Tools Serving Metadata Data Management Kale Fairing TFX Airflow, + KF Pipelines HP Tuning Tensorboard KFServing Seldon Core TFServing, + Training Operators Pytorch XGBoost, + Tensorflow Prometheus Versioning ReproducibilitySecure Sharing

8. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Platforms / clouds GCP AWS IBM CloudAzure OpenShift Istio ML tools PyTorch scikit-learn Jupyter TensorFlow PyTorch Serving TensorFlow Serving XGBoost Kubernetes Argo Prometheus Spartakus Seldon Core Kubeflow applications and scaffolding Chainer MPI MXNet On prem Jupyter notebook web app and controller Hyperparameter tuning (Katib) Kale Pipelines Metadata Training operators: MPI, MXNet, PyTorch, TFJob, XGBoost Kubeflow UI KFServing 8

9. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Platforms / clouds Kubeflow applications and scaffolding ML tools PyTorch scikit-learn Jupyter TensorFlow XGBoost Chainer MPI MXNet GCP AWS IBM CloudAzure OpenShift Istio PyTorch Serving TensorFlow Serving Kubernetes Argo Prometheus Spartakus Seldon Core On prem Jupyter notebook web app and controller Hyperparameter tuning (Katib) Kale Pipelines Metadata Kubeflow UI KFServing Training operators: MPI, MXNet, PyTorch, TFJob, XGBoost 9

10. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Platforms / clouds ML tools PyTorch scikit-learn Jupyter TensorFlow XGBoost Kubeflow applications and scaffolding Chainer MPI MXNet GCP AWS IBM CloudAzure OpenShift Istio PyTorch Serving TensorFlow Serving Kubernetes Argo Prometheus Spartakus Seldon Core On prem Jupyter notebook web app and controller Hyperparameter tuning (Katib) Kale Pipelines Metadata Kubeflow UI KFServing Training operators: MPI, MXNet, PyTorch, TFJob, XGBoost 10

11. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Platforms / clouds ML tools PyTorch scikit-learn Jupyter TensorFlow XGBoost Kubeflow applications and scaffolding Chainer MPI MXNet GCP AWS IBM CloudAzure OpenShift Istio PyTorch Serving TensorFlow Serving Kubernetes Argo Prometheus Spartakus Seldon Core Jupyter notebook web app and controller Hyperparameter tuning (Katib) Kale Pipelines Metadata Kubeflow UI KFServing On prem Training operators: MPI, MXNet, PyTorch, TFJob, XGBoost 11

12. Simplify. Accelerate. Collaborate. arrik.to/odsc20 ML workflow Identify problem and collect and analyse data Choose an ML algorithm and code your model Experiment with data and model training Tune the model hyperparamet ers Jupyter Notebook Katib TensorFlow scikit-learn PyTorch XGBoost Jupyter Notebook Kale Pipelines KFServing PyTorch TFServing Seldon Core NVIDIA TensorRT Serve the model for online/batch prediction 12

13. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Testimonials ● Dyson: “Kubeflow is to data science what a lab notebook is to biomedical scientists — a way to expedite ideas from the lab to the ‘bedside’ 3x faster, while ensuring experimental reproducibility.” ● US Bank: “The Kubeflow 1.0 release is a significant milestone as it positions Kubeflow to be a viable ML Enterprise platform. Kubeflow 1.0 delivers material productivity enhancements for ML researchers.” ● One Technologies: “With Kubeflow at the heart of our ML platform, our small company has been able to stack models in production to improve CR, find new customers, and present the right product to the right customer at the right time.” 13

14. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Testimonials ● GroupBy: “Kubeflow is helping GroupBy in standardizing ML workflows and simplifying very complicated deployments!” ● Volvo Cars: “Kubeflow provides a seamless interface to a great set of tools that together manages the complexity of ML workflows and encourages best practices. The Data Science and Machine Learning teams at Volvo Cars are able to iterate and deliver reproducible, production grade services with ease.” 14

15. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Kubeflow - The Infra Side ● Install ● Manage ● Secure ● Upgrade

16. Simplify. Accelerate. Collaborate. arrik.to/odsc20 What is GitOps 16 All configuration state is declaratively stored in git.

17. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Imperative vs Declarative Imperative 1. Create Service 2. Update LoadBalancer 3. Upgrade Deployment

18. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Imperative vs Declarative Declarative Desired State (YAML) K8s kind: Pod metadata: name: mysql spec: image: mysql:7.6 apply etcd

19. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Controller Spec (desired) Status (real) Kubernetes Objects Controller Pattern - The driver behind declarative APIs Used everywhere in Kubernetes Observe Calculate Reconcile Physical ResourcesPhysical ResourcesPhysical Resources write

20. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Why GitOps?

21. Simplify. Accelerate. Collaborate. arrik.to/odsc20 K8s etcd K8s etcd Reproducibility commit 856df4gdf56g4561d1fg564df5g61v6854df Author: yanniszark <yanniszark@arrikto.com> Date: Tuesday, Sep 8 11:24:12 2020 +0200 Upgrade MySQL to new version. K8s etcd apply ● Whole configuration state in git, versioned by commits ● Careful! Mutable state still outside of git (e.g., volumes, S3) ○ Need versioning solution for end-to-end reproducibility ○ Arrikto Rok produces data commits for your volumes (e.g., MySQL)

22. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Rollbacks commit 856df4gdf56g4561d1fg564df5g61v6854df Author: yanniszark <yanniszark@arrikto.com> Date: Tuesday, Sep 8 11:24:12 2020 +0200 Upgrade MySQL to new version. commit er1f1ef8f1e1rf5641sdfs564d1fsd1f5sd61fgwd Author: yanniszark <yanniszark@arrikto.com> Date: Tuesday, Sep 4 15:24:12 2020 +0200 Increase MySQL read-replicas to 3 for higher availability. git log K8s etcd apply apply Unhealthy

23. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Auditing git blame 48f078b0 (Yannis Zarkadas 2020-06-11 41) kind: Deployment 48f078b0 (Yannis Zarkadas 2020-06-11 42) metadata: 48f078b0 (Yannis Zarkadas 2020-06-11 43) name: nginx 48f078b0 (Yannis Zarkadas 2020-06-11 46) spec: 48f078b0 (Stefano Fioravanzo 2020-06-11 47) replicas: 1

24. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Rich Ecosystem ● Collaboration through familiar and battle-tested tools ○ Pull Requests and Code Reviews ● Rich offerings ○ GitHub, GitLab, etc. ● Plenty of integrations ○ GitHub Actions, GitLab Pipelines, etc. Reuse whatever you already know about git!

25. Simplify. Accelerate. Collaborate. arrik.to/odsc20 GitOps Workflow

26. Simplify. Accelerate. Collaborate. arrik.to/odsc20 GitOps Workflow Deployer GitOps repo commit kubectl apply Desired State (YAML) kind: Pod metadata: name: mysql spec: image: mysql:7.6

27. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Deployer GitOps repo (downstream) commit kubectl apply GitOps Workflow ● What about 3rd-party applications? ● Usually, infrastructure configuration is provided by the vendor ● For example, Kubeflow maintains a “manifests” monorepo with all deployment configurations manifests repo (upstream) Kubeflow Developer commit periodic rebase

28. Simplify. Accelerate. Collaborate. arrik.to/odsc20 GitOps - Managing Configuration ● How do you manage configuration? ○ Use 3rd-party provided configs ○ Customer changes ○ Update periodically ● Several tools: ○ helm ○ kustomize ○ ... ● Kubeflow uses kustomize ● We (Arrikto) use kustomize for our deployments kind: Deployment metadata: name: redis namespace: deploy spec: template: spec: image: gcr.io/redis:6 replicas: 3

29. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Managing Configuration - Helm ● Helm is the most popular tool that uses templating ● Exposes knobs to consumers via values file ● Templating is hard to read values.yaml Chart Customer Repo (downstream) Vendor Repo (upstream)

30. Simplify. Accelerate. Collaborate. arrik.to/odsc20 {{ if (or (not .Values.persistence.enabled) (eq .Values.persistence.type "pvc")) }} apiVersion: apps/v1 kind: Deployment metadata: name: {{ template "grafana.fullname" . }} namespace: {{ template "grafana.namespace" . }} labels: {{- include "grafana.labels" . | nindent 4 }} {{- if .Values.labels }} {{ toYaml .Values.labels | indent 4 }} {{- end }} {{- with .Values.annotations }} annotations: {{ toYaml . | indent 4 }} {{- end }} https://github.com/helm/charts/blob/99805df25da220c379ad609fcb7cf20e5e0d4fc0/stable/grafana/templates/deployment.yaml Managing Configuration - Templating

31. Simplify. Accelerate. Collaborate. arrik.to/odsc20 └── redis ├── base │ ├── configmap.yaml │ ├── kustomization.yaml │ ├── service.yaml │ └── statefulset.yaml Managing Configuration - kustomize resources: - configmap.yaml - service.yaml - statefulset.yaml kustomization.yaml ● Base configuration

32. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Managing Configuration - kustomize kind: Deployment metadata: name: redis spec: template: spec: image: gcr.io/redis:6 replicas: 1 kustomize build redis/base resources: - configmap.yaml - service.yaml - statefulset.yaml kustomization.yaml

33. Simplify. Accelerate. Collaborate. arrik.to/odsc20 └── redis ├── base └── overlays ├── deploy │ ├── kustomization.yaml │ └── patches │ └── replicas.yaml Managing Configuration - kustomize bases: - ../base namespace: deploy patches: - path: patches/replicas.yaml kustomization.yaml ● Create overlays (variants) to customize deployment kind: Deployment metadata: name: redis spec: template: spec: replicas: 3 patches/replicas.yaml

34. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Managing Configuration - kustomize kind: Deployment metadata: name: redis namespace: deploy spec: template: spec: image: gcr.io/redis:6 replicas: 3 kustomize build redis/overlays/deploy bases: - ../base namespace: deploy patches: - path: patches/replicas.yaml kustomization.yaml kind: Deployment metadata: name: redis spec: template: spec: replicas: 3 patches/replicas.yaml

35. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Managing Configuration - kustomize Vendor Repo (upstream) Customer Repo (downstream) v1 v2 d1 v1 v2

36. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Managing Configuration - kustomize Vendor Repo (upstream) Customer Repo (downstream) v1 v2 d1 v1 v2 v3

37. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Managing Configuration - kustomize Vendor Repo (upstream) Customer Repo (downstream) d1 v1 v2 v3 v1 v2 v3 ● Update with git rebase ● Separate file == no conflicts

38. Simplify. Accelerate. Collaborate. arrik.to/odsc20 └── redis ├── base └── overlays ├── deploy Managing Configuration - kustomize ● Powerful customization capabilities ● Rebase from upstream to get new updates ● Customizations in separate folders, no conflicts on rebase Consumer customizations Upstream repo GitOps repo

39. Simplify. Accelerate. Collaborate. arrik.to/odsc20 ● Simplify Kubeflow stack installation, configuration, and management ○ Deploy and manage software in a declarative way ○ Complete visibility of system configuration ● Accelerate the upgrade process by continuously deploying changes to the cluster ○ Track changes and revert if something goes wrong ● Collaborate better and faster, share knowledge with the whole team ○ Keep using your favorite familiar tools and workflow Why GitOps in your Kubeflow Deployment 39

40. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Demo 1. Kubernetes Cluster (EKS) on Amazon Web Services 2. Deploy Rok 3. Deploy Kubeflow 4. Update installation from upstream

41. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Security in Kubeflow “We observed that this attack effected on tens of Kubernetes clusters.”

42. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Multi-User Isolation Authentication? Authorization?

43. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Authentication using OIDC Protocol ● Open & Standardized OAuth Flow ● Objective: Get the User’s Identity (username, groups) ● Popular and Secure

44. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Identity Provider LDAP / AD Static Password File External IdP (Google, LinkedIn, …) OIDC Provider Interface

45. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Authorization ● Authorization with Role Based Access Control (RBAC) ● Commit RBAC resources in git for reproducibility Endpoints RBAC Resources Verbs GET /apis/kubeflow.org/v1/notebooks/{name} Notebooks GET GET /apis/kubeflow.org/v1/notebooks Notebooks LIST POST /apis/kubeflow.org/v1/notebooks Notebooks CREATE DELETE /apis/kubeflow.org/v1/notebooks/{name} Notebooks DELETE GET /apis/kubeflow.org/v1/experiments/{name} Experiments GET Can USER do ACTION on RESOURCE in NAMESPACE?

46. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Handling Credentials ● Credentials are kept in Secrets ● Injected into Pods at runtime with PodDefaults ● Applications expect to find secrets in files or environment variables

47. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Auth Guidelines for Kubeflow ● Guidelines for secure applications in Kubeflow https://github.com/kubeflow/community/blob/3357efef4947297026111df17e468d9204fa2061/guidelines/auth.md

48. Simplify. Accelerate. Collaborate. arrik.to/odsc20 CI/CD for ML How can data scientists continually improve and validate models? ● Develop models and pipelines in Jupyter ● Convert notebook to pipeline using Kale ● Run pipeline using Kubeflow Pipelines ● Explore and debug pipeline using Rok Develop (Jupyter) Explore Pipeline (Rok) Create Pipeline (Kale) Run Pipeline (KF Pipelines) N2P CUJ 48

49. Simplify. Accelerate. Collaborate. arrik.to/odsc20 This workshop will focus on two essential aspects: • Low barrier to entry: deploy a Jupyter Notebook to Kubeflow Pipelines in the Cloud using a fully GUI-based approach • Reproducibility: automatic data versioning to enable reproducibility and better collaboration between data scientists Data Science with Kubeflow Building a Model Logging Data Ingestion Data Analysis Data Transform -ation Data Validation Data Splitting Trainer Model Validation Training At Scale Roll-out Serving Monitoring Kubeflow Pipelines exists because Data Science and ML are inherently pipeline processes 49

50. Simplify. Accelerate. Collaborate. arrik.to/odsc20 This workshop will focus on two essential aspects: • Low barrier to entry: deploy a Jupyter Notebook to Kubeflow Pipelines in the Cloud using a fully GUI-based approach • Reproducibility: automatic data versioning to enable reproducibility and better collaboration between data scientists Data Science with Kubeflow Building a Model Logging Data Ingestion Data Analysis Data Transform -ation Data Validation Data Splitting Trainer Model Validation Training At Scale Roll-out Serving Monitoring Kubeflow Pipelines exists because Data Science and ML are inherently pipeline processes 50

51. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Benefits of running a Notebook as a Pipeline ● The steps of the workflow are clearly defined ● Parallelization & isolation ○ Hyperparameter tuning ● Data versioning ● Different infrastructure requirements ○ Different hardware (GPU/CPU) 51

52. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Before Amend your ML code? Write your ML code Create Docker images Write DSL KFP code Compile DSL KFP Upload pipeline to KFP Run the Pipeline Workflow 52

53. Simplify. Accelerate. Collaborate. arrik.to/odsc20 After Amend your ML code? Write your ML code Tag your Notebook cells Run the Pipeline at the click of a button Just edit your Notebook! Before Amend your ML code? Write your ML code Create Docker images Write DSL KFP code Compile DSL KFP Upload pipeline to KFP Run the Pipeline Workflow 53

54. Simplify. Accelerate. Collaborate. arrik.to/odsc20 After Amend your ML code? Write your ML code Tag your Notebook cells Run the Pipeline at the click of a button Just edit your Notebook! Before Amend your ML code? Write your ML code Create Docker images Write DSL KFP code Compile DSL KFP Upload pipeline to KFP Run the Pipeline Workflow A Data Scientist can now reduce the time taken to write ML code and run a pipeline by 70%. That means you can now run 3x as many experiments as you did before. What that really means is that you can deliver work faster to the business and drive more revenue 54

55. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Hyperparameter optimization The two ways of life ● Change the parameters manually ● Use Katib 55

56. Simplify. Accelerate. Collaborate. arrik.to/odsc20 What is Katib Katib is a Kubernetes-based system for Hyperparameter Tuning and Neural Architecture Search. It supports a number of ML frameworks, including TensorFlow, Apache MXNet, PyTorch, XGBoost, and others. 56

57. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Hyperparameter optimization Combining the N2P CUJ with Katib ● Configure parameters, search algorithm, and objectives using a GUI ● Start HP tuning with the click of a button ● Reproducibility of every pipeline and every step ● Run Katib Trials as Pipelines ● Complete visibility of every different Katib Trial ● Caching for faster computation 57

58. Simplify. Accelerate. Collaborate. arrik.to/odsc20 A data science journey 58

59. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Agenda Convert notebook to a Kubeflow pipeline Explore Kubeflow components Explore the ML code of the dog breed identification example Explore the accuracy of the various models Optimize a model with hyperparameter tuning Explore the results of HP tuning 21 3 54 6 Go to arrik.to/demowfhp to find the Codelab with the step-by-step instructions for this tutorial 59

60. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Agenda Convert notebook to a Kubeflow pipeline Explore Kubeflow components Explore the ML code of the dog breed identification example Explore the accuracy of the various models Optimize a model with hyperparameter tuning Explore the results of HP tuning 21 3 54 6 60

61. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Agenda Explore Kubeflow components Explore the ML code of the dog breed identification example Explore the accuracy of the various models Optimize a model with hyperparameter tuning Explore the results of HP tuning 21 3 54 6 Convert notebook to a Kubeflow pipeline 61

63. Simplify. Accelerate. Collaborate. arrik.to/odsc20 KALE – Kubeflow Automated Pipelines Engine ● Python package + JupyterLab extension ● Convert a Jupyter Notebook to a KFP workflow ● No need for Kubeflow SDK Annotated Jupyter Notebook Kale Conversion Engine 63

64. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Kale Modules Parse Analyze Marshal Generate Derive pipeline structure Identify dependencies Inject data objects Generate & deploy pipeline 64

65. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Contribute github.com/kubeflow-kale 65

66. Simplify. Accelerate. Collaborate. arrik.to/odsc20 TFDV TFTransform TFDV Estimators TFΜΑ TFServing Katib Tuner Arrikto Rok 66

67. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Arrikto Rok Data Versioning, Packaging, and Sharing Across teams and cloud boundaries for complete Reproducibility, Provenance, and Portability ProductionExperimentation Training Any Storage Any Storage Any Storage Data-aware PVCs Data-aware PVCs Data-aware PVCs Arrikto Arrikto Arrikto CSI CSI CSI 67

68. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Data Lake Step 1 Step 2 Step 3 1. Download data from Lake 2. Store it locally 3. Do initial analysis 4. Upload data to Lake 5. Download data from Lake 6. Store it locally 7. Transform data 8. Upload to Lake 9. Download data from Lake 10. Store it locally 11. Train model 12. Upload Model Building without Data Management 71

69. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Step 1 Step 2 Step 3 1. Clone disk from snapshot 2. Do initial analysis 3. Snapshot 4. Clone disk of Step 1 5. Transform data 7. Clone disk of Step 2 8. Train model Rok 6. Snapshot 9. Snapshot Object Store Model Building with Local Data Management (Rok) 72

70. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Step 1 Step 2 Step 3 Arrikto Object Store Step 1 Step 2 Step 3 Arrikto Object Store Step 4 Step 5 Step 6 Arrikto Object Store Location 2 Pipeline 2: Start after Step 3 of Pipeline 1 Pipeline 3: Reproduce Pipeline 1 Location 1 Pipeline 1 Sync State & Data 73

71. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Validation Preprocessing Training Data Cloned Data Evaluation DeploymentTraining Fail Validated Data Preprocessed Data Trained Model Evaluated Model Deployed Model Arrikto Rok 74

75. Simplify. Accelerate. Collaborate. arrik.to/odsc20 What have we achieved in this tutorial? ● Streamline your ML workflows using intuitive UIs ● Exploit the caching feature to give a boost to your pipeline runs ● Run a pipeline-based hyperparameter tuning workflow starting from your Jupyter Notebook ● Use Kale as a workflow tool to orchestrate Katib and Kubeflow Pipelines experiments ● Simplify the deployment and management of Kubeflow using GitOps ● Accelerate the time to production ● Collaborate faster and more easily in a secure and isolated manner Summary 78

76. Simplify. Accelerate. Collaborate. arrik.to/odsc20 79 Just a small sample of community contributions ● Jupyter manager UI ● Pipelines volume support ● MiniKF ● Auth with Istio + Dex ● On-premise installation ● Linux Kernel

77. Simplify. Accelerate. Collaborate. arrik.to/odsc20 80 Community Kubeflow is open ● Open community ● Open design ● Open source ● Open to ideas Get involved ● github.com/kubeflow ● kubeflow.slack.com ● @kubeflow ● kubeflow-discuss@googlegroups.com ● Community call on Tuesdays

78. Simplify. Accelerate. Collaborate. arrik.to/odsc20 Thank You! More Info arrik.to/odsc20 Email Address: stefano@arrikto.com yanniszark@arrikto.com company/arrikto Arrikto Arrikto Arrikto

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