Remove Data Collection Remove Experimentation Remove Internet of Things
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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.

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Glossary of Digital Terminology for Career Relevance

Rocket-Powered Data Science

Computer Vision: Data Mining: Data Science: Application of scientific method to discovery from data (including Statistics, Machine Learning, data visualization, exploratory data analysis, experimentation, and more). See [link]. Edge Computing (and Edge Analytics): Industry 4.0: Industry 4.0 Industry 4.0

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How Svevia connects roads, risk, and refuse through the cloud

CIO Business Intelligence

Taking out the trash Division Drift has been key to disruptively digitize Svevia’s remit with the help of the internet of things (IoT), data collection, and data analysis. Not for experiments For a company like Svevia, there’s no room for experimentation, underlines Wester. “We

Risk 98
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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Keep in mind that data science is fundamentally interdisciplinary. Let’s look through some antidotes.