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]

Machine Learning 197

Machine Learning Modeling Testing Risk Management 197

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

Modeling 59

Modeling Metadata Publishing Sales 59

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.

Modeling 77

Modeling Machine Learning Deep Learning Testing 77

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].)

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

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.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

AWS Big Data

MARCH 13, 2024

For building any generative AI application, enriching the large language models (LLMs) with new data is imperative. Ray cluster for ingestion and creating vector embeddings In our testing, we found that the GPUs make the biggest impact to performance when creating the embeddings. This breaks out to approximately 200 per record.

Data Processing 114

Data Processing Dashboards Machine Learning Metrics 114

AWS Big Data

NOVEMBER 29, 2023

In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift , the first fully-managed, petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools.

Data Warehouse 104

Data Warehouse Data Lake Analytics Machine Learning 104