the data science andai lifecycle - deutsche messe...
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The Data Science and AI lifecycle
Stephan ReimannIBM [email protected]
stereimann
IBM Cloud / DEVELOPERS @ CEBIT - Data Science and AI Lifecycle / June 2018 / © 2018 IBM Corporation
Our journey today
CRISP-DMAn industry standard process fordata science
2 Build the machine learning model
3 Deploy the machine learning model
4 Learning never stops – continuous learning
1 Prepare & understand the data Usually 80%of the effort
The Cool stuffeverybody talks about
Just developerstuff?
The lifecyclehas more steps
IBM Cloud / DEVELOPERS @ CEBIT - Data Science and AI Lifecycle / June 2018 / © 2018 IBM Corporation 2
1
Prepare & understand thedata
&
2
Build the machine learningmodel
3IBM Cloud / DEVELOPERS @ CEBIT - Data Science and AI Lifecycle / June 2018 / © 2018 IBM Corporation
Step 1: Build the model
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Data Science can be done in many ways:
Result:A machine learning model that describes theinsights learned from the data.
IBM Cloud / DEVELOPERS @ CEBIT - Data Science and AI Lifecycle / June 2018 / © 2018 IBM Corporation
3
Deploy the machine learningmodel
5IBM Cloud / DEVELOPERS @ CEBIT - Data Science and AI Lifecycle / June 2018 / © 2018 IBM Corporation
Deployment overview
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Different ways:Rest API, Batch, Streaming
Target: • Apply the learnings to new data = scoring• Integrate machine learning into processes and
applications
Different ways to deploy:
Service with automated model management ...
Build your own API, e.g. using Flask or Function as a Service
IBM Cloud / DEVELOPERS @ CEBIT - Data Science and AI Lifecycle / June 2018 / © 2018 IBM Corporation
A practical example
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Code: https://datascience.ibm.com/exchange/public/entry/view/db078b55b82aee7146210a087cb22f89
Tutorial:https://datascience.ibm.com/docs/content/analyze-data/ml-bluemix-app.html?cm_sp=dw-dwtv-_-data-science-_-putting-face-machine-learning
IBM Cloud / DEVELOPERS @ CEBIT - Data Science and AI Lifecycle / June 2018 / © 2018 IBM Corporation
Deployment aspects
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• Infrastructure
• Availability
• Automation
• Catalog / Maintain Model information
• Versioning
You just have gotten into DevOpstopics
IBM Cloud / DEVELOPERS @ CEBIT - Data Science and AI Lifecycle / June 2018 / © 2018 IBM Corporation
4
Learning never stops –continuous learning
9IBM Cloud / DEVELOPERS @ CEBIT - Data Science and AI Lifecycle / June 2018 / © 2018 IBM Corporation
Motivation
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Companies are realizing that in many settings machine learning models start degrading soon after they get deployed to production.
(https://www.oreilly.com/ideas/why-continuous-learning-is-key-to-ai )
https://www.slideshare.net/DavidTalby/when-models-go-rogue-hard-earned-lessons-about-using-machine-learning-in-production
IBM Cloud / DEVELOPERS @ CEBIT - Data Science and AI Lifecycle / June 2018 / © 2018 IBM Corporation
A few words about methods
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https://www.slideshare.net/DavidTalby/when-models-go-rogue-hard-earned-lessons-about-using-machine-learning-in-production
IBM Cloud / DEVELOPERS @ CEBIT - Data Science and AI Lifecycle / June 2018 / © 2018 IBM Corporation
Step 3: Continuous Learning
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Target: Ensure accurate predictionsAs humans, we are continuously learning, machine learning models should do the same to cope with an ever changing world
https://medium.com/ibm-data-science-experience/continuous-learning-on-watson-data-platform-cc39f3fd5042
IBM Cloud / DEVELOPERS @ CEBIT - Data Science and AI Lifecycle / June 2018 / © 2018 IBM Corporation
Key take away
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Never forget to update yourmachine learning models overtime!&Automate!!!!
It is easier than you think!IBM Cloud / DEVELOPERS @ CEBIT - Data Science and AI Lifecycle / June 2018 / © 2018 IBM Corporation