This post is a re-post from my original LinkedIn post
Over the past few years I had the unique opportunity to see a start-up, TubeMogul, going through hyper-growth, an IPO, and an acquisition by a fortune 500, Adobe. In this journey, I was exposed to a lot of technical challenges, and I work on systems at an astonishing scale, i.e. over 350 billions real-time bidding request a day. It allowed me to build some strong personal opinions on the role of an SRE and how they can help transform an organization. I'm lucky enough to work with a talented team of SRE that keep pushing the limits of innovation while executing through chaos.
As I flew back from the ML for DevOps (Houston)
I.T. Systems, with the broad adoption of public and private cloud, get more complex over time. The hyper-adoption of micro-services and the increase of loosely coupled distributed systems are an obvious factor, though you can see how IoT devices, edge computing, and al. can factor into the mix.
Point being, it is increasingly difficult for a single individual to understand the space in which a product evolve and live. One cannot assume knowing it all. Humans quickly reach their cognitive limit. So, how do SRE overcome this limit? Below is my take on the top 5 machine learning and self-healing techniques used by SRE to scale and operate increasingly complex environments.
Version 2 of GNU Bash
In this post we will review how to declare, iterate over, and check a value of an indexed arrays and associative arrays.
Over the past decade I had the privilege to build a massive scale infrastructure at a small start-up called TubeMogul
Today, I got the privilege to present my team work at USENIX LISA 15