Subject archive for "model-production," page 3
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.
By Nick Elprin10 min read
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.
By Nick Elprin10 min read
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 the whitepaper, "Introducing Model Management". The whitepaper is a companion to his talk and is also available for download.
By Domino3 min read
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 for download. A distilled slide deck that serves as a complement to the report is also available.
By Domino10 min read
The Machine Learning Reproducibility Crisis
Are We Back in the Dark Ages? Without Source Control?
By Pete Warden9 min read
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 interactive breakouts. The learnings, anecdotes, and best practices shared in the workshop were based upon years of candid discussions with customers about managing and accelerating data science work. The workshop also featured reusable templates that included a pre-flight data science project checklist as well as a planning template for hiring and onboarding data scientists. We are sharing the breakout materials based on attendee feedback. If you missed Strata and are interested in joining similar discussions, then consider attending Rev.
By Domino5 min read
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