Notes on machine learning in product

1. Engineering starts with infrastructure. Ville Tuulos gave a great overview of the relationship between data science and infrastructure at Netflix. https://lnkd.in/gF7-2jt 2. What and how to monitor ML systems in the wild. Josh Wills gave an excellent deep-dive into DevOps meets Data Science based on his experience at Google, Cloudera, and Slack. https://lnkd.in/gWpByUY 3. Deploying ML is easy. Deploying it reliably is hard. Daniel Papasian and Todd Underwood analyzed post mortems of 96 ML systems outages at Google and found that most outages are not ML-centric and are more related to the distributed character of the pipeline. https://lnkd.in/gsCsRaj 4. Martin Casado and Matt Bornstein gave an interesting perspective on the economics of AI, how cloud services are reducing the margin, scaling problem due to edge cases, and the diminishing return of added data https://lnkd.in/gggp6q2