Four short links: 26 May 2017
Service Availability, Data Share, Eventual Consistency Explained, and Reproducible Deep Learning
- The Calculus of Service Availability — A service cannot be more available than the intersection of all its critical dependencies. If your service aims to offer 99.99% availability, then all of your critical dependencies must be significantly more than 99.99% available. Internally at Google, we use the following rule of thumb: critical dependencies must offer one additional 9 relative to your service—in the example case, 99.999% availability—because any service will have several critical dependencies, as well as its own idiosyncratic problems. This is called the “rule of the extra 9.”
- datproject — open source crypto—guaranteed distributed data share, designed for versioned data sets.
- How Your Data is Stored — eventual consistency VERY LUCIDLY explained. It follows the original (entertaining) paper by Leslie Lamport but spells everything out clearly for non-computer-scientists.
- OpenAI Baselines — open source implementations of the interesting published algorithms in deep learning. The papers often gloss over some of the details, so a full and working implementation truly lets others build on research. It’s like the reproducibility project for deep learning.
