Business Objectives Data mining Experimentation Metrics 
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. We must therefore maintain statistical rigor in quantifying experimental uncertainty. In this post we explore how and why we can be “ data-rich but information-poor ”. And an LSOS is awash in data, right?
Rocket-Powered Data Science
JULY 6, 2021
2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase. But the power, value, and imperative of observability does not stop there.
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