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Tag: Model Production

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, consistency and performance in the future. Many thanks to Don Miner for collaborating with Domino on this article. For additional vital signs and insight beyond what …
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 Pete Skomoroch’s “Product Management for AI” session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and …
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 in Production. This chapter excerpt provides data scientists with insights and tradeoffs to consider when moving machine learning models to production. Also, if you’re interested in learning about how Domino provides …
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. Domino’s Head of Content sat down with Don Miner and Marshall Presser to discuss the state of collaboration between data science and data engineering. The blog post provides distilled insights, …
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 projects, interesting research, great examples, amazing people, etc. – all pointed at how to level up your organization’s data science practices. Key Theme: Machine Learning Models Themes. Amidst the flurry …
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 over and over: 1) Companies are recognizing that data science is a competitive differentiator. 2) People are worried their companies are falling behind — that …
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 also delved into how data science leaders can utilize model management and help their companies become successful model-driven organizations. This blog post provides a distilled summary of …
Model Evaluation
This Domino Data Science Field Note provides some highlights of Alice Zheng’s report, “Evaluating Machine Learning Models“, including evaluation metrics for supervised learning models and offline evaluation mechanisms. The full in-depth report also includes coverage on offline vs online evaluation mechanisms, hyperparameter tuning and potential A/B testing pitfalls is available …
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 acquired by Google. He is also an Apple alumnus and blogs at petewarden.com. This post candidly discusses some of the real world reproducibility challenges …