Rental Cars and Industrialized Learning to Rank with Sean Downes
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The document discusses the challenges and principles of industrializing data science workflows in cloud infrastructure, highlighting the need for clarity around data ownership and organization. It presents idiosyncratic operating principles that emphasize the distinct roles of engineers, product managers, and data scientists. The presentation also stresses the importance of fostering a data-centric culture and closing the loop on data utilization.
Rental Cars and Industrialized Learning to Rank with Sean Downes
- 1. … and associated Idiosyncratic Operating Principles. Industrializing DataScience Workflows Sean Downes Sr DataScientist @ Expedia, Inc.
- 2. The Problem So you’ve been asked to bring the infrastructure into the cloud. So your Data Lake is actually a Data Swamp.
- 4. Login Impressions Clicks Purchases The Problem Every Line of Business has its own Structure Every MicroService has a log And you want to A|B Test
- 5. Can you please turn… The Problem into using
- 6. Preview Context / Disclaimer Lightning Review of Data Platforms (idiosyncratic) Organizing Principles
- 7. Context / Disclaimer Academic Theoretical Physicist We’ve got some work to do. So… I’m implicitly assuming this talk will be one in an ensemble of opinions
- 8. supercomputers… Lightning Review of Data Platforms
- 9. … the Commerical Data Center… Lightning Review of Data Platforms
- 10. … Virtualized Everything Lightning Review of Data Platforms 1. Assign Tasks their own virtual hardware 2. Expend / Contract Resources by demand 3. Real-time HotSwapping 4. Software Updates Built In 5. Etc Etc Etc.
- 11. idiosyncratic Organizing Principles iOP1) Clarity iOP2) Engineers are not Data Scientists iOP3) PMs are not Data Scientists iOP4) Data Scientists are not Engineers iOP5) Close the Data Loop
- 12. iOP1: Data Clarity PUBLISH THIS INTERNALLY! “big data, big noise” Where is what data? Who owns what field? What is this this field? Where did this field go? Why is this field NULL?
- 13. iOP1: Data Clarity Minibatch streaming into nested JSON? O(10kB)? GZip? O(50-500 MB) Parquet. Snappy. “Expect Data Science” Spark Big Thanks to Jason Pohl @ DB! And Charles Pritchard!
- 14. iOP2: Engineers are not Data Scientists “why would you need to do that?” Scratch Space Cluster Bootstrap Permissions Access S3 Buckets Sandbox Clusters Share Notebooks Across Accounts We DO NOT SPEAK IAM Role/Anything
- 15. iOP2: Engineers are not Data Scientists “why would you need to do that?” if possible: Write your own Pipelines. else: Explain Data Science.
- 16. iOP3: PMs are not Data Scientists “you don’t need that!” Once upon a time in the Flight DataLake… Only 10% of a Search Impression was recorded Worse: It was only the Cheapest10% Many of the bookings where not included in this list!
- 17. iOP4: Data Scientists are Not Engineers “we need to support models in @#&%? format” Pick a Robust Standard and Stick to It. If you’re big enough to worry about this, you can commit code jPMML Everybody Use Git. Now. Yes You. Production Code Matters. Format. Document. Pipelines Count as Production Code.
- 18. iOP5: Close that Data Loop What is your data doing? New Data? Consider Bandits! Big Data? Set up a learning problem. Empower by Design.
- 20. Contact information or call to action goes here. Thank You.