Some resources that I’ve found really helpful to understand machine learning in production.

1. Engineering starts with infrastructure. @vtuulos gave a great overview of the relationship between data science and infrastructure at Netflix https://youtu.be/XV5VGddmP24 ">https://youtu.be/XV5VGddmP...
3. Deploying ML is easy. Deploying it reliably is hard. Daniel Papasian and @tmu analyzed post mortems of 96 ML systems outages at Google and found that most outages are even ML and more related to the distributed character of the pipeline https://www.youtube.com/watch?v=hBMHohkRgAA&ab_channel=USENIX">https://www.youtube.com/watch...
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