A presentation at DevOpsDays Chicago in in Chicago, IL, USA by Lilia Gutnik
Data Science + Magic = Profit LILIA GUTNIK @superlilia
credit: United States Nuclear Regulatory Commission, 1979
credit: United States Nuclear Regulatory Commission, 1979
credit: " cascading failure of humans " –Roman Mars, 99% Invisible Podcast
credit: Interviewing Users: HOW TO UNCOVER COMPELLING INSIGHTS by Steve Portigal
credit: Interviewing Users: HOW TO UNCOVER COMPELLING INSIGHTS by Steve Portigal
Interviewing Users HOW TO UNCOVER COMPELLING INSIGHTS by Steve Portigal
credit: Schur, M. (Writer, Director). (2017). Michael’s Gambit [Season 1, Episode 13]. In M. Schur (Executive producer), The Good Place. Universal City, CA: NBC.
Schur, M. (Writer, Director). (2017). Michael’s Gambit [Season 1, Episode 13]. In M. Schur (Executive producer), The Good Place. Universal City, CA: NBC. Data Science + Magic = Profit
credit: http://futurama.wikia.com/wiki/File:Fry_Sleeping.png Schur, M. (Writer, Director). (2017). Michael’s Gambit [Season 1, Episode 13]. In M. Schur (Executive producer), The Good Place. Universal City, CA: NBC.
credit: http://futurama.wikia.com/wiki/File:Fry_Sleeping.png Schur, M. (Writer, Director). (2017). Michael’s Gambit [Season 1, Episode 13]. In M. Schur (Executive producer), The Good Place. Universal City, CA: NBC.
credit for concept: Linda Zhou, Western Digital
credit concept: Linda Zhou, Western Digital
credit: Jeremy Schiff, MLConf 2015
credit: United States Nuclear Regulatory Commission, 1979
credit: United States Nuclear Regulatory Commission, 1979
credit: United States Nuclear Regulatory Commission, 1979
350 million incidents 589 million notifications 1.7 million people
350 million incidents 589 million notifications 1.7 million people 28,000 manual merges 1/100 of 1% of incidents in dataset
Collaborative Filtering for Implicit Feedback Datasets, Y.Hu, Y. Koren, C. Volinsky, 2008 Eighth IEEE International Conference on Data Mining
30 seconds 3 hours $200.00 @superlilia Data Science + Magic = Profit
Complexity Accuracy Performance Precision Sellability Scale Customer Expectation Resilience Recall Explainability
Creativity Customers Design Experiment Data Science Define success Iterate Make it understandable Ship it
Data Science + Magic = Profit by Lilia Gutnik @superlilia
NRC Annual Report, 1979, United States Nuclear Regulatory Commission •“Lessons from Three Mile Island: the Design of Interactions in a High Stakes Environment”, A. Roesler, University of Washington, 2009 •Machine Learning for Designers, Patrick Hebron, 2016, O’Reilly Media, Inc •“Collaborative Filtering for Implicit Feedback Datasets”, Y.Hu, Y. Koren, C. Volinsky, 2008 Eighth IEEE International Conference on Data Mining •Human Interpretable Machine Learning - the Need and Importance of Model Interpretation, D. Sarkar, Intel, 2018, kdnuggets.com •Satisficing and Optimizing Metric, DeepLearning.ai, A. Ng, coursera.org, •Recommendation Architecture, J. Schiff, MLConf 2015, New York, NY •Simplify Machine Learning Pipeline Analysis with Object Storage, L. Zhou, westerndigital.com, 2018
You can’t throw a rock without running into a talk about the basics of data science and best practices for data hygiene. What’s next? Shipping a data science-powered feature takes more than data collection, experimentation, and analysis - it takes magic. This talk will teach the magic based on the many ~mistakes~ learning opportunities that my team has experienced so that any team will feel inspired to build a customer-facing data science-based feature.
What is different about designing, developing, shipping, supporting, and going on-call for a data science-powered feature? How can you deliver customer value and, yes, make money with data science? Data science models provide insights; a product provides value. You don’t have to be a data scientist to develop a data science-based feature. You’ll leave this talk inspired and prepared to build a successful data science-based feature with these five crucial considerations:
What are potential features your team could build that use data science and deliver value to our customers?
What does it take to productionalize a data science model?
How do you go on call for a data science-based feature that is (by nature) different for every customer?
What are the design considerations for a feature that has unpredictable (non-deterministic) behavior?
How do you develop a feature that relies on math you (probably) do not completely understand?
Here’s what was said about this presentation on social media.
for free. You
can too.