Data Science Blog | Model Production

Machine Learning Modeling: How It Works and Why It’s Important

Models are the central output of data science, and they have tremendous power to transform companies, industries, and society. At the center of...

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The Role of Containers on MLOps and Model Production

Container technology has changed the way data science gets done. The original container use case for data science focused on what I call,...

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Bringing ML to Agriculture: Transforming a Millennia-old Industry

Guest post by Jeff Melching from The Climate Corporation At The Climate Corporation, we aim to help farmers better understand their operations and...

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On Being Model-driven: Metrics and Monitoring

This article covers a couple of key Machine Learning (ML) vital signs to consider when tracking ML models in production to ensure model reliability,...

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Product Management for AI

Pete Skomoroch presented “Product Management for AI” at Rev. This post provides a distilled summary, video, and full transcript. Session Summary ...

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Machine Learning in Production: Software Architecture

Special thanks to Addison-Wesley Professional for permission to excerpt the following "Software Architecture" chapter from the book, Machine Learning...

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Themes and Conferences per Pacoid, Episode 7

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Introduction Welcome...

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Collaboration Between Data Science and Data Engineering: True or False?

This blog post includes candid insights about addressing tension points that arise when people collaborate on developing and deploying models....

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Themes and Conferences per Pacoid, Episode 1

Introduction: New Monthly Series! Welcome to a new monthly series! I’ll summarize highlights from recent industry conferences, new open source...

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Model Management and the Era of the Model-Driven Business

Over the past few years, we’ve seen a new community of data science leaders emerge. Regardless of their industry, we have heard three themes emerge...

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Put Models at the Core of Business Processes

At Rev, Nick Elprin, Domino's CEO, continued to provide insights on managing data science based upon years of candid discussions with customers. He...

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Model Evaluation

This Domino Data Science Field Note provides some highlights of Alice Zheng’s report, "Evaluating Machine Learning Models", including evaluation...

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The Machine Learning Reproducibility Crisis

Pete Warden is the Technical Lead on the TensorFlow Mobile Embedded Team at Google doing Deep Learning. He is formerly the CTO of Jetpac, which was...

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Managing Data Science as a Capability

Nick Elprin, CEO at Domino, presented a 3-hour training workshop, “Managing Data Science in the Enterprise”, that provided practical insights and...

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A Guide to Machine Learning Model