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Gilbane Advisor 8-18-21 — Graph thinking, machine learning ops

The Gilbane Advisor
Hello Reader:
This week we have articles from Paco Nathan (@pacoid)and Kabir Nagrecha (@KabirNagrecha). News comes from Bloomreach, MarkLogic, Acquia, OpenAI, and Moonwalk Universal and IBM.

Opinion / Analysis
Often the most difficult and costly step before promising but complex enterprise technologies becomes mainstream is for organizations to figure out how to operationalize them. Knowledge graphs and machine learning are both at this stage. How do they fit in with existing software, systems, processes, and resources? What has to change, what will it cost, how long will it take?
Big questions, but both articles this week should be useful when you’re thinking about strategies. First, Kabir Nagrecha discusses how existing systems research and practices contribute to machine learning operations (MLOps). Next, Paco Nathan looks at ways to think about knowledge graphs in the context of systems thinking and complex problem solving.
ML system architecture from D. Sculley's “Hidden Technical Debt in Machine Learning Systems”. Note the relative importance of data management versus ML code.
ML system architecture from D. Sculley's “Hidden Technical Debt in Machine Learning Systems”. Note the relative importance of data management versus ML code.
Ambrose: knowledge organization by level of expertise
Ambrose: knowledge organization by level of expertise
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Frank Gilbane
Frank Gilbane @gilbane

The Gilbane Advisor is curated for content technology, computing, and digital experience professionals. The focus is on strategic technologies. We publish recommended articles and content technology news weekly. See all issues from 2014 to today at https://gilbane.com/gilbane-advisor-index/

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