Data Science deals with the extraction of valuable insights from an incredible number of sources in an endless number of formats. This session will go through a typical workflow using practical tools and tricks. This will give you a basic understanding of Data Science in the Cloud. The examples will show the steps that are needed to build and deploy a model to predict traffic collisions with weather data.
14. Normal Day
325TB per day
2.4 Billion API requests every
15 minutes
50,000 videos played
@MargrietGr
The Weather Company
Hurricane Harvey
500TB per day
3.4 Billion API requests every
15 minutes
750,000 videos played
48. Publish Results – PixieApp in a notebook
@MargrietGr
PixieApps
Dashboards within a notebook
49. Why Data Science to the Cloud?
@MargrietGr
Scales up – unlimited resources
All tools connected
Github integration
Local development, easy to move
Collaboration!
50. Thank you!
Dr. Margriet Groenendijk
Developer Advocate
mgroenen@uk.ibm.com
@MargrietGr
Slides
https://www.slideshare.net/MargrietGroenen
dijk/presentations
Blog
https://medium.com/ibm-watson-data-lab
@MargrietGr
IBM Data Science Experience
https://datascience.ibm.com
PixieDust
https://ibm-cds-labs.github.io/pixiedust/
Notebooks
https://github.com/ibm-cds-labs/python-
notebooks
Weather Data
https://business.weather.com/products/weather-
data-packages
IBM Bluemix
https://console.ng.bluemix.net/