A typical machine learning pipeline begins as a series of preprocessing steps followed by experimentation, optimization and model-tuning, and, finally deployment. Jupyter notebooks have become a hugely popular tool for data scientists and other machine learning practitioners to explore and experiment as part of this workflow, due to the flexibility and interactivity they provide. However, with notebooks it is often a challenge to move from the experimentation phase to creating a robust, modular and production-grade end-to-end AI pipeline.

1. Notebook-based AI Pipelines with
Elyra and Kubeflow
Nick Pentreath
Principal Engineer, IBM
@MLnick

2. About
DEG / Nov 18, 2020 / © 2020 IBM Corporation
– @MLnick on Twitter, Github, LinkedIn
– Principal Engineer, IBM CODAIT (Center for
Open-Source Data & AI Technologies)
– Machine Learning & AI
– Apache Spark committer & PMC
– Author of Machine Learning with Spark
– Various conferences & meetups
2

3. Improving the Enterprise AI Lifecycle in Open Source
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– CODAIT aims to make AI solutions
dramatically easier to create,
deploy, and manage in the
enterprise.
– We contribute to and advocate for
the open-source technologies that
are foundational to IBM’s AI
offerings.
– 30+ open-source developers!
Center for Open Source Data & AI Technologies
codait.org
CODAIT
Open Source @ IBM

4. Agenda
4
– Machine learning workflow
– JupyerLab & Elyra
– Demo
– Conclusion
DEG / Nov 18, 2020 / © 2020 IBM Corporation

5. Machine Learning
Workflow
5
Data Analyze Process Train Deploy
Predict
&
Maintain
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6. Workflow spans teams …
6
Data Analyze Process Train Deploy
Predict
&
Maintain
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Data Engineers Data Scientists & Researchers
Machine Learning &
Production Engineers

7. … and tools
7
Data Analyze Process Train Deploy
DEG / Nov 18, 2020 / © 2020 IBM Corporation
Data formats
• CSV, SQL
• JSON,
Parquet,
AVRO
• Binary
(image,
audio)
• …
Data Engineers Data Scientists & Researchers
Machine Learning &
Production Engineers
Analysis & data
viz
• ggplot
• dplyr
• matplotlib
• Pandas
• SparkSQL
• …
Pre-processing
& pipelines
• dplyr
• pandas
• scikit-learn
• SparkSQL /
SparkML
• …
Frameworks
• R, scikit-
learn
• SparkML
• TensorFlow
• PyTorch
• LightGBM,
XGBoost
• …
Formats &
mechanisms
• Variety of
formats
• Containers
• …

8. Iteration &
Experimentation
8
Data Analyze Process Train Deploy
DEG / Nov 18, 2020 / © 2020 IBM Corporation
Data Scientists & Researchers
Load Clean Explore Interpret
Refine

9. Iteration &
Experimentation
9
Data Process Train Deploy
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Data Scientists & Researchers
Extract
features
Pre-
process
Train Evaluate
Refine
Analyze

10. Interactive Notebooks
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Notebooks have become
the de-facto standard
for content-rich,
interactive & iterative
work
* Logos trademarks of their respective projects

11. Elyra Overview
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Elyra is a set of AI-
centric extensions to
JupyterLab Notebooks
* Logos trademarks of their respective projects

12. Elyra Key Features
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– Visual Pipeline Editor
Visual editor for building AI pipelines,
enabling the conversion of multiple
notebooks into batch jobs or workflows.
– Notebooks as batch jobs
– Python script execution
– Automated Table of Contents
– Code Snippets
– Git integration

13. Elyra Key Features
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– Visual Pipeline Editor
– Notebooks as batch jobs
Extends the notebook UI to simplify the
submission of notebooks as a batch job
for model training
– Python script execution
– Automated Table of Contents
– Code Snippets
– Git integration

14. Elyra Key Features
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– Visual Pipeline Editor
– Notebooks as batch jobs
– Python script execution
Edit and execute python scripts against
local or cloud-based resources
– Automated Table of Contents
– Code Snippets
– Git integration

15. Elyra Key Features
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– Visual Pipeline Editor
– Notebooks as batch jobs
– Python script execution
– Automated Table of Contents
Generate & navigate table of contents
from notebooks & python scripts
– Code Snippets
– Git integration

16. Elyra Key Features
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– Visual Pipeline Editor
– Notebooks as batch jobs
– Python script execution
– Automated Table of Contents
– Code Snippets
Easy creation and insertion of reusable
code snippets for various languages
– Git integration

17. Elyra Key Features
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– Visual Pipeline Editor
– Notebooks as batch jobs
– Python script execution
– Automated Table of Contents
– Code Snippets
– Git integration
Track project changes and share among
teammates

18. DEG / Nov 18, 2020 / © 2020 IBM Corporation
Getting started with Elyra
1. Try Elyra from Binder
ibm.biz/elyra-demo
2. Run Elyra from Docker
ibm.biz/elyra-docker-installation
3. Install Elyra on your local machine
ibm.biz/elyra-installation
18

19. DEG / Nov 18, 2020 / © 2020 IBM Corporation 19

20. Start using Elyra today!
Getting started with Elyra
ibm.biz/elyra-installation
Elyra on Github
github.com/elyra-ai/elyra
Elyra Notebook projects on Github
github.com/CODAIT/flight-delay-notebooks
github.com/CODAIT/covid-notebooks
Contributing to the projects
• Star and fork, submit bug reports, suggest improvements,
help with code reviews, join our community meetings
ibm.biz/elyra-demo
gitter.im/elyra-ai/community
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21. Thank you
codait.org
twitter.com/codait_org
github.com/CODAIT
developer.ibm.com
21DEG / Nov 18, 2020 / © 2020 IBM Corporation
Check out the Data Asset Exchange
https://ibm.biz/data-exchange
Sign up for IBM Cloud
https://ibm.biz/Bdqkfg

22. DEG / Nov 18, 2020 / © 2020 IBM Corporation 22

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Don’t forget to rate
and review the sessions.