Subject archive for "code," page 5

Notebooks are increasingly crucial in the data scientist's toolbox. Although considered relatively new, their history traces back to systems like Mathematica and MATLAB. This form of interactive workflow was introduced to assist data scientists in documenting their work, facilitating reproducibility, and prompting collaboration with their team members. Recently there has been an influx of newcomers, and data scientists now have a wide range of implementations to choose from, such as Jupyter Notebook, Zeppelin, R Markdown, Spark Notebook, and Polynote.

Dean Wampler provides a distilled overview of Ray, an open source system for scaling Python systems from single machines to large clusters. If you are interested in additional insights, register for the upcoming Ray Summit.

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.

This article provides insight on the mindset, approach, and tools to consider when solving a real-world ML problem. It covers questions to consider as well as collecting, prepping and plotting data.

This article delves into methods for analyzing multivariate and univariate time series data.