O'Reilly on Data

JUNE 18, 2019

These data sets are often siloed, incomplete, and extremely sparse. Moreover, the domain knowledge, which often is not encoded in the data (nor fully documented), is an integral part of this data (see this article from Forbes). See this article on data integration status for details.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

Sisense

APRIL 10, 2020

We’ll actually do this later in this article. These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling. Some standard Python libraries are Pandas, Numpy, Scikit-Learn, SciPy, and Matplotlib. R libraries.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Cloudera

FEBRUARY 16, 2022

Model interpretability is one of five main components of model governance. The complete list is shown below: Model Lineage . Model Visibility. Model Explainability. Model Interpretability. Model Reproducibility. In this article, we explore model governance, a function of ML Operations (MLOps).

Machine Learning 84

Machine Learning Modeling Metadata Recreation/Entertainment 84

Rocket-Powered Data Science

JULY 6, 2021

I provide below my perspective on what was interesting, innovative, and influential in my watch list of the Top 10 data innovation trends during 2020. 1) Automated Narrative Text Generation tools became incredibly good in 2020, being able to create scary good “deep fake” articles.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139