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49:01
AI, ML & Data Engineering Follow 1002 Followers

End-to-End ML without a Data Scientist

Posted by Holden Karau  on  May 30, 2018 Posted by Holden Karau Follow 3 Followers  on  May 30, 2018

Holden Karau discusses how to train models, and how to serve them, including basic validation techniques, A/B tests, and the importance of keeping models up-to-date.

38:53
AI, ML & Data Engineering Follow 1002 Followers

Liquidity Modeling in Real Estate Using Survival Analysis

Posted by David Lundgren  on  May 29, 2018 Posted by David Lundgren Follow 0 Followers , Xinlu Huang Follow 0 Followers  on  May 29, 2018

Xinlu Huang and David Lundgren discuss hazard and survival modeling, metrics, and data censoring, describing how Opendoor uses these models to estimate holding times for homes and mitigate risk.

50:45
AI, ML & Data Engineering Follow 1002 Followers

The Black Swan of Perfectly Interpretable Models

Posted by Leah McGuire  on  May 22, 2018 Posted by Leah McGuire Follow 0 Followers , Mayukh Bhaowal Follow 0 Followers  on  May 22, 2018

Mayukh Bhaowal, Leah McGuire discuss how Salesforce Einstein made ML more transparent and less of a black box, and how they managed to drive wider adoption of ML.

49:20
AI, ML & Data Engineering Follow 1002 Followers

Machine Learning Pipeline for Real-Time Forecasting @Uber Marketplace

Posted by Danny Yuan  on  May 10, 2018 Posted by Danny Yuan Follow 3 Followers , Chong Sun Follow 0 Followers  on  May 10, 2018

Chong Sun and Danny Yuan discuss how Uber is using ML to improve their forecasting models, the architecture of their ML platform, and lessons learned running it in production.

21:58
AI, ML & Data Engineering Follow 1002 Followers

Causal Modeling Using Software Called TETRAD V

Posted by Suchitra Abel  on  Oct 04, 2017 Posted by Suchitra Abel Follow 0 Followers  on  Oct 04, 2017

Suchitra Abel introduces TETRAD and some of its components used for causal modeling to find out the proper causes and effects of an event.

44:35
AI, ML & Data Engineering Follow 1002 Followers

Evaluating Machine Learning Models: A Case Study

Posted by Nelson Ray  on  Oct 04, 2017 Posted by Nelson Ray Follow 0 Followers  on  Oct 04, 2017

Nelson Ray talks about on how to estimate the business impact of launching various machine learning models, in particular, those Opendoor uses for modeling the liquidity of houses.

38:23
AI, ML & Data Engineering Follow 1002 Followers

When Models Go Rogue: Hard Earned Lessons on Using Machine Learning in Production

Posted by David Talby  on  Aug 19, 2017 Posted by David Talby Follow 0 Followers  on  Aug 19, 2017

David Talby summarizes best practices & lessons learned in ML, based on nearly a decade of experience building & operating ML systems at Fortune 500 companies across several industries.

27:57
AI, ML & Data Engineering Follow 1002 Followers

Causal Inference in Data Science

Posted by Amit Sharma  on  Aug 11, 2017 Posted by Amit Sharma Follow 0 Followers  on  Aug 11, 2017

Amit Sharma discusses the value of counterfactual reasoning and causal inference, demonstrating that relying on predictive modeling based on correlations can be counterproductive.

48:32
AI, ML & Data Engineering Follow 1002 Followers

Predictability in ML Applications

Posted by Claudia Perlich  on  Mar 23, 2017 Posted by Claudia Perlich Follow 0 Followers  on  Mar 23, 2017

Claudia Perlich presents scenarios in which the combination of different and highly informative features can have significantly negative overall impact on the usefulness of predictive modeling.

45:21
Architecture & Design Follow 2422 Followers

Framing Our Potential for Failure

Posted by Michelle Brush  on  Mar 18, 2017 Posted by Michelle Brush Follow 0 Followers  on  Mar 18, 2017

Michelle Brush discusses modeling complex systems and architectural changes that could introduce new modes of failure, using examples from embedded systems to large stream processing pipelines.

36:16
Architecture & Design Follow 2422 Followers

Papyrus for Real Time: Executable Modeling on Eclipse

Posted by Charles Rivet  on  Jul 07, 2016 Posted by Charles Rivet Follow 0 Followers  on  Jul 07, 2016

Charles Rivet introduces Papyrus RT, an industrial-grade modeling environment for the development of complex, software intensive, real-time, embedded, cyber-physical systems.

49:35
AI, ML & Data Engineering Follow 1002 Followers

Startup ML: Bootstrapping a Fraud Detection System

Posted by Michael Manapat  on  Apr 16, 2016 Posted by Michael Manapat Follow 2 Followers  on  Apr 16, 2016

Michael Manapat talks about how to choose, train, and evaluate models, how to bridge the gap between training and production systems, and avoiding pitfalls.

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