The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. But the fact that a service could have millions of users and billions of interactions gives rise to both big data and methods which are effective with big data. we have $K(alpha,beta)=21.01$.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

The Unofficial Google Data Science Blog

JULY 22, 2020

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Domino Data Lab

JUNE 15, 2021

If Big Data has taught us anything, it is that with large volumes and high velocity data, it is advisable to move the computation to where the data resides. We can now test the function from our Domino Workspace (JupyterLab in this case): cur.execute("SELECT ADD(5,2)") cur.fetchone()[0]. About Domino.

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69