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AI meets operations

O'Reilly

First, the behavior of an AI application depends on a model , which is built from source code and training data. A model isn’t source code, and it isn’t data; it’s an artifact built from the two. This means that, to have a history of how an application was developed, you have to look at more than the source code.

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5 key areas for tech leaders to watch in 2020

O'Reilly

It’s also the data source for our annual usage study, which examines the most-used topics and the top search terms. [1]. This combination of usage and search affords a contextual view that encompasses not only the tools, techniques, and technologies that members are actively using, but also the areas they’re gathering information about.

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Organise your engineering teams around the work by reteaming

Abhishek Tiwari

As Steve Jobs wisely said, Don’t Be Trapped by Dogma – Which is Living With the Results of Other People’s Thinking In my view, technology executives and engineering leaders are overly obsessed with the Spotify model. Specialisation could be around products, business process, or technologies. And there lies the problem.

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The death of Agile?

O'Reilly

It’s not about getting software developers to write code faster. Perhaps the appropriate yardstick for AI projects is the experiment itself, not the code committed to git.). Software architecture, infrastructure, and operations are each changing rapidly. Key survey results: The C-suite is engaged with data quality.