Subject archive for "data-science," page 9

Data Science

Bringing Machine Learning to Agriculture

At The Climate Corporation, we aim to help farmers better understand their operations and make better decisions to increase their crop yields in a sustainable way. We’ve developed a model-driven software platform, called Climate FieldView™, that captures, visualizes, and analyzes a vast array of data for farmers and provides new insight and personalized recommendations to maximize crop yield. FieldView™ can incorporate grower-specific data, such as historical harvest data and operational data streaming in from special devices, including (our FieldView Drive) that are installed in tractors, combines, and other farming equipment. It incorporates public and third-party data sets, such as weather, soil, satellite, elevation data and proprietary data, such as genetic information of seed hybrids that we acquire from our parent company, Bayer.

By Jeff Melching10 min read

Data Science

The Curse of Dimensionality

Danger of Big Data

By Bill Shannon14 min read

Data Science

Providing fine-grained, trusted access to enterprise datasets with Okera and Domino

Domino and Okera - Provide data scientists access to trusted datasets within reproducible and instantly provisioned computational environments.

By David Bloch8 min read

Data Science

Why models fail to deliver value and what you can do about it.

Building models requires a lot of time and effort. Data scientists can spend weeks just trying to find, capture and transform data into decent features for models, not to mention many cycles of training, tuning, and tweaking models so they’re performant.

By David Bloch9 min read

Data Science

The Importance of Structure, Coding Style, and Refactoring in Notebooks

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.

By Nikolay Manchev26 min read

Data Science

Domino Paves the Way for the Future of Enterprise Data Science with Latest Release

Today, we announced the latest release of Domino’s data science platform which represents a big step forward for enterprise data science teams. We’re introducing groundbreaking new features – including On-demand Spark clusters, enhanced project management, and the ability to export models – that give enterprises unprecedented power to scale their data science capabilities by addressing common struggles.

By Nick Elprin11 min read

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