Kubeflow is an open-source project that makes deploying machine learning workflows on Kubernetes simple and scalable. It provides components for machine learning tasks like notebooks, model training, serving, and pipelines. Kubeflow started as a Google side project but is now used by many companies like Spotify, Cisco, and Itaú for machine learning operations. It allows running workflows defined in notebooks or pipelines as Kubernetes jobs and serves models for production.

1. Kubeflow
Karane Vieira - Dev

2. Machine Learning
● Machine do what We TELL them to do:
● With Machine Learning, we can TELL
machines how to LEARN what to do:
Observations

3. Machine Learning: Applications
Face Detection and Face
Recognition
Spam Detection Chat Bots
Self Driving Cars Recommendation Systems

4. Machine Learning: Common steps
Prototyping
Training
Deploy Server
Dataset preparation
ETL
Feature Engineering

5. Kubernetes
● Platform to manage containerized workloads and services
● Supports declarative configuration and automation
● Service discovery and load balancing, storage orchestration, automatic
self-healing, rollouts and rollbacks
● It comprises most of the features of other known devops tools (i.e. ansible,
puppet, etc)

6. Kubeflow: Definition & Why
● Project dedicated to make deployments of machine learning
workflows on Kubernetes simple, portable and scalable
● Anywhere you run kubernetes, you should be able to run
kubeflow
● Kubeflow provides a set of components that facilitate working
with machine learning, from training to deploy.

7. Kubeflow: Who uses it
● Started as side-project in Google
● Today is an open-source
● Google is the main sponsor of the project
● It is getting a lot of traction
● Spotify, Itaú, Cisco, Melio, etc
Melio
MLOps company

8. Kubeflow: Main features
1. Notebooks
2. Model training: Fairing
3. Model serving
4. Pipelines

9. Kubeflow: Jupyter
● Jupyter is a simple python IDE in the browser
● Jupyter is commonly used by Data Scientists
● In Kubeflow you can run many Jupyter servers

10. Kubeflow: Fairing
Notebook -
Training Notebook -
Training
● Takes a training notebook, transforms it a docker image and runs it as a
Job in Kubernetes

11. Kubeflow: Pipeline
● Pipelines are ML Workflows that can be defined inside Kubeflow
● You can upload and share it on Kubeflow Pipeline UI
● Example of components: data preprocessing, training transformation, model
training, etc
● Each component runs in a separate POD

13. Kubeflow: other features

14. Use Case - Spotify

16. Use Case - Cisco

17. Kubeflow: Pipeline

19. Use Case - Itaú Unibanco

21. Kubeflow: observations
● Kubeflow is in its early days
● There are still some points to be stabilized
● Connect with your cloud providers resources may give you a little headache

22. Kubeflow: Hands-on
Record a video and play here -- just the Jupyter example
Steps:
1. Create a Jupyter pod
2. Connect to Jupyter
3. Open a machine learning example
4. Show it running

23. Kubeflow: Hands-on 2
Show somethings live: get nodes, get pods, cloud9, etc

24. Kubeflow cases
● https://blogs.cisco.com/cloud/how-to-optimize-your-k8s-infrastructure-for-ai-ml-development-with-a-f
ew-clicks
● https://cloud.google.com/blog/products/ai-machine-learning/itau-unibanco-how-we-built-a-cicd-pipeli
ne-for-machine-learning-with-online-training-in-kubeflow
● https://engineering.atspotify.com/2019/12/13/the-winding-road-to-better-machine-learning-infrastruct
ure-through-tensorflow-extended-and-kubeflow/