Add timestamps: Computations as Assets (PyData Global 2021)

pydata-global-2021/computations-as-assets.md

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+Video URL: https://www.youtube.com/watch?v=J4zvLJn3ya8
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+## Contents
+
+00:01:22 Introduction  
+00:02:03 Exax history: From startup to eBay to open source  
+00:02:55 Two design goals: Minimise risk for manual mistakes and maximise efficiency  
+00:03:34 The Backblaze dataset example  
+00:04:23 Backblaze dataset: Introduction to animation  
+00:05:37 Backblaze dataset: Animation starts  
+00:06:27 A program execution  
+00:06:58 Collect everything  
+00:07:38 Reuse (Only compute when seeing something new)  
+00:08:33 Input parameters can be links to existing jobs  
+00:09:17 Compare traditional tools to Exax  
+00:10:16 Jobs reflect different stages of source code  
+00:11:01 A key idea  
+00:11:38 Introduction to transparency and reproducibility  
+00:11:47 Transparency  
+00:12:22 Transparency examples  
+00:12:45 Reproducibility  
+00:13:56 Backblaze dataset: Back to example  
+00:14:33 Backblaze dataset: Intermediate files  
+00:15:56 Backblaze dataset: Execution times  
+00:17:53 NYC Yellow Taxi trip dataset example  
+00:18:18 Taxi dataset: Quality  
+00:18:50 Taxi dataset: January 2016 US blizzard  
+00:19:09 Taxi dataset: Introduction to animation  
+00:19:20 Taxi dataset: Animation starts (COVID impact on taxi business)  
+00:20:07 Taxi dataset: Execution times  
+00:20:46 Other features  
+00:21:02 High-performance data access  
+00:23:13 Parallel processing and data locality  
+00:24:50 Conclusion  
+00:25:01 Links to GitHub and resources