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Best Practice of Using Data Science Competitions Skills to Improve Business Value

DataRobot Blog

This article presents a case study of how DataRobot was able to achieve high accuracy and low cost by actually using techniques learned through Data Science Competitions in the process of solving a DataRobot customer’s problem. Sensor Data Analysis Examples. The Best Way to Achieve Both Accuracy and Cost Control.

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Of Muffins and Machine Learning Models

Cloudera

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

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DataRobot Notebooks: Enhanced Code-First Experience for Rapid AI Experimentation

DataRobot Blog

ML model builders spend a ton of time running multiple experiments in a data science notebook environment before moving the well-tested and robust models from those experiments to a secure, production-grade environment for general consumption. Capabilities Beyond Classic Jupyter for End-to-end Experimentation.

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Top 10 Data Innovation Trends During 2020

Rocket-Powered Data Science

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase. And the goodness doesn’t stop there.

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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

Data scientists and researchers require an extensive array of techniques, packages, and tools to accelerate core work flow tasks including prepping, processing, and analyzing data. Utilizing NLP helps researchers and data scientists complete core tasks faster. Natural Language Processing.] together at Stanford University.

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6 Case Studies on The Benefits of Business Intelligence And Analytics

datapine

It’s all about using data to get a clearer understanding of reality so that your company can make more strategically sound decisions (instead of relying only on gut instinct or corporate inertia). Ultimately, business intelligence and analytics are about much more than the technology used to gather and analyze data.