O'Reilly on Data
DECEMBER 12, 2019
Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]
Smart Data Collective
MARCH 27, 2020
Statistics show that married people have fewer car accidents than singletons. Insurance companies have access to crime statistics and can track the number of car theft and break-ins per neighborhood. Insurance companies have access to stats on what make and model of car is stolen more often or involved in more crashes.
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Insight
MARCH 12, 2020
In 2013, Robert Galbraith?—?an The AIgent was built with BERT, Google’s state-of-the-art language model. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. More relevant to the AIgent is Google’s BERT model, a task-agnostic (i.e. an aspiring author?—?finished
Domino Data Lab
DECEMBER 1, 2019
This article covers how to detect data drift for models that ingest image data as their input in order to prevent their silent degradation in production. Introduction: preventing silent model degradation in production. This article explores an approach that can be used to detect data drift for models that classify/score image data.
Domino Data Lab
JANUARY 6, 2019
I’ve been teaching data science since 2008 privately for employers – exec staff, investors, IT teams, and the data teams I’ve led – and since 2013, for industry professionals in general. Also, clearly there’s no “one size fits all” educational model for data science. The Berkeley model addresses large university needs in the US.
The Unofficial Google Data Science Blog
APRIL 23, 2024
If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate. And sometimes even if it is not[1].)
Domino Data Lab
APRIL 21, 2021
We’ll use a gradient boosting technique via XGBoost to create a model and I’ll walk you through steps you can take to avoid overfitting and build a model that is fit for purpose and ready for production. Let’s also look at the basic descriptive statistics for all attributes. from sklearn import metrics.
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