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Kubeflow: Machine Learning en Cloud para todos

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Kubeflow: Machine Learning en Cloud para todos

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Speaker: Juan Camilo Díaz
Video: https://youtu.be/jfH93vdRmTk

Kubeflow hace que implementar workflows de Machine Learning en Kubernetes sean simples, portátiles y escalables. Kubeflow es el kit de herramientas que permite implementar procesos de Machine Learning, ampliando la capacidad de Kubernetes para ejecutar pasos independientes y configurables, con bibliotecas y frameworks específicos.

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Hay trabajos y hay carreras. Las oportunidades vienen a golpear la puerta cuando menos lo esperas. La decisión es tuya. Desde tener la oportunidad de hacer algo significativo día tras día, hasta estar rodeado de gente supremamente inteligente y motivada.

¿Estás listo?
Descúbre todas nuestras oportunidades acá: https://bit.ly/2PWKky9

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Síguenos en:

Facebook: https://www.facebook.com/Globant/
Twitter: https://twitter.com/Globant
Instagram: https://www.instagram.com/globantpics/
Linkedin: https://www.linkedin.com/company/globant

Speaker: Juan Camilo Díaz
Video: https://youtu.be/jfH93vdRmTk

Kubeflow hace que implementar workflows de Machine Learning en Kubernetes sean simples, portátiles y escalables. Kubeflow es el kit de herramientas que permite implementar procesos de Machine Learning, ampliando la capacidad de Kubernetes para ejecutar pasos independientes y configurables, con bibliotecas y frameworks específicos.

---------------------------------------------------------------------------------------------------------------------------------------------------------------

Hay trabajos y hay carreras. Las oportunidades vienen a golpear la puerta cuando menos lo esperas. La decisión es tuya. Desde tener la oportunidad de hacer algo significativo día tras día, hasta estar rodeado de gente supremamente inteligente y motivada.

¿Estás listo?
Descúbre todas nuestras oportunidades acá: https://bit.ly/2PWKky9

---------------------------------------------------------------------------------------------------------------------------------------------------------------
Síguenos en:

Facebook: https://www.facebook.com/Globant/
Twitter: https://twitter.com/Globant
Instagram: https://www.instagram.com/globantpics/
Linkedin: https://www.linkedin.com/company/globant

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Kubeflow: Machine Learning en Cloud para todos

  1. 1. Kubeflow Globant @ Medellín - Colombia Sept 2020
  2. 2. JUAN CAMILO DÍAZ 4.5 años en Globant Acerca de mí ... Juan Camilo Díaz Ortega Big Data Architect at Globant Data & Analytics Studio Kubeflow
  3. 3. Why it is so painful to deploy Machine Learning workflows?
  4. 4. Kubeflow Machine Learning Workflow Gathering data Data pre-processing Researching the model that will be best for the type of data Training and testing the model Evaluation
  5. 5. Kubeflow Machine Learning Workflow - Real World
  6. 6. Kubeflow Machine Learning Workflow
  7. 7. Kubeflow Machine Learning Workflow
  8. 8. Kubeflow Machine Learning Workflow
  9. 9. Containers, Kubernetes. What are we talking about ?
  10. 10. Kubeflow Containers Containers are technologies that allow you to package and isolate applications along with the entire runtime environment, that is, with all the files that Containers require to run Allows you to move the application that is inside the container between the environments (development, test, production, etc.), without losing any of its functions.
  11. 11. Kubeflow Containers
  12. 12. Kubeflow Kubernetes Kubernetes (also known as k8s or "kube") is an open-source system for automating deployment, scaling, and management of containerized applications. Container orchestration platform. In other words, you can cluster together groups of containers, and Kubernetes helps you easily and efficiently manage those clusters. Kubernetes clusters can span hosts across on-premise, public, private, or hybrid clouds. For this reason, Kubernetes is an ideal platform for hosting cloud-native applications that require rapid scaling
  13. 13. Kubeflow Kubernetes Orchestrate containers across multiple hosts. Scale containerized applications and their resources on the fly Control and automate application deployments and updates Health-check and self-heal your apps with autoplacement, autorestart, autoreplication, and autoscaling.
  14. 14. DefinitionKubeflow Kubeflow is an open source Kubernetes-native platform for developing, orchestrating, deploying, and running scalable and portable machine learning workloads Portable Machine Learning Stack The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. https://www.kubeflow.org/docs/about/kubeflow/
  15. 15. Kubeflow componentsKubeflow
  16. 16. Kubeflow Changing the dev and deployment process
  17. 17. Kubeflow Machine Learning Workflow
  18. 18. Kubeflow Agenda
  19. 19. Kubeflow Components
  20. 20. ksonnetKubeflow https://ksonnet.io/
  21. 21. ksonnetKubeflow https://ksonnet.io/ Jsonnet library A data templating language
  22. 22. Central DashboardKubeflow Kubeflow user interfaces (UIs)
  23. 23. MetadataKubeflow Help Kubeflow users understand and manage their machine learning workflows
  24. 24. MetadataKubeflow
  25. 25. Jupyter NotebooksKubeflow Using Jupyter notebooks in Kubeflow
  26. 26. Jupyter NotebooksKubeflow Using Jupyter notebooks in Kubeflow
  27. 27. Jupyter NotebooksKubeflow
  28. 28. Jupyter NotebooksKubeflow
  29. 29. PipelinesKubeflow Kubeflow Pipelines is a platform for building and deploying portable and scalable end-to-end ML workflows, based on containers. Code that performs one step in the Pipeline. In other words a containerized implementation of an ML task.
  30. 30. PipelinesKubeflow A pipeline is a description of an ML Workflow It runs a containers which provide portability, repeatability and encapsulation, which is able to decouples the execution environment to code runtime.
  31. 31. PipelinesKubeflow
  32. 32. PipelinesKubeflow
  33. 33. PipelinesKubeflow
  34. 34. PipelinesKubeflow
  35. 35. Frameworks for TrainingKubeflow MPI Operator
  36. 36. Tools for Serving ML Models - KFServingKubeflow
  37. 37. Tools for Serving ML Models - Seldon Core ServingKubeflow
  38. 38. Tools for Serving ML Models - BentoKubeflow
  39. 39. Katib - Hyperparameter TuningKubeflow Hyperparameters are the variables that control the model training process. For example: ● Learning rate. ● Number of layers in a neural network. ● Number of nodes in each layer.
  40. 40. Katib - Hyperparameter TuningKubeflow
  41. 41. Katib - Hyperparameter TuningKubeflow
  42. 42. Katib - Hyperparameter TuningKubeflow
  43. 43. Google Cloud Demo
  44. 44. Where I can Start ?
  45. 45. Cloud Computing - Cloud Providers 12 months of popular free services + $200 credit to explore Azure for 30 days + Always free 25+ services https://azure.microsoft.com/en-us/free/ 12 months free services + Short-term free trial offers start from the date you activate a particular service + Always free, free tier offers do not expire and are available to all AWS customers https://aws.amazon.com/free/ 12 months free services + $300 free credit + Always free products, which provides limited access to many common Google Cloud resources, free of charge. https://cloud.google.com/free https://docs.microsoft.com/en-us/azure/ https://cloud.google.com/docs https://docs.aws.amazon.com/index.html Kubeflow
  46. 46. Kubeflow resourcesKubeflow https://www.kubeflow.org/ https://www.kubeflow.org/docs/ Getting started with Kubeflow
  47. 47. ¿Seguimos en contacto? @jcamilodo https://www.linkedin.com/in/jcamilodo/ Cloud & Big Data
  48. 48. THANK YOU

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