Remove Data Collection Remove Data Science Remove Experimentation Remove ROI
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Machine Learning Product Management: Lessons Learned

Domino Data Lab

This Domino Data Science Field Note covers Pete Skomoroch ’s recent Strata London talk. Pete indicates, in both his November 2018 and Strata London talks, that ML requires a more experimental approach than traditional software engineering. These steps also reflect the experimental nature of ML product management.

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Themes and Conferences per Pacoid, Episode 6

Domino Data Lab

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science.

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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. – back to the structure of the dataset. Let’s look through some antidotes.

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Product Management for AI

Domino Data Lab

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. And then you’ll do a lot of work to get it out and then there’ll be no ROI at the end.

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6 Case Studies on The Benefits of Business Intelligence And Analytics

datapine

Because things are changing and becoming more competitive in every sector of business, the benefits of business intelligence and proper use of data analytics are key to outperforming the competition. Ultimately, business intelligence and analytics are about much more than the technology used to gather and analyze data.