  Controlled experimentation, also called A/B testing, is widely adopted to
accelerate product innovations in the online world. However, how fast we
innovate can be limited by how we run experiments. Most experiments go through
a "ramp up" process where we gradually increase the traffic to the new
treatment to 100%. We have seen huge inefficiency and risk in how experiments
are ramped, and it is getting in the way of innovation. This can go both ways:
we ramp too slowly and much time and resource is wasted; or we ramp too fast
and suboptimal decisions are made. In this paper, we build up a ramping
framework that can effectively balance among Speed, Quality and Risk (SQR). We
start out by identifying the top common mistakes experimenters make, and then
introduce the four SQR principles corresponding to the four ramp phases of an
experiment. To truly scale SQR to all experiments, we develop a statistical
algorithm that is embedded into the process of running every experiment to
automatically recommend ramp decisions. Finally, to complete the whole picture,
we briefly cover the auto-ramp engineering infrastructure that can collect
inputs and execute on the recommendations timely and reliably.
