Remove Data Engineering Remove Software Architecture Remove Testing
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The death of Agile?

O'Reilly

Fetishizing unit testing. The most important is discovering how to work with data science and artificial intelligence projects. Development timelines for these projects aren’t as predictable as traditional software; they stretch the meaning of “testing” in strange ways; they aren’t deterministic.

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AI meets operations

O'Reilly

On one hand, ops groups are in a good position to do this; they’re already heavily invested in testing, monitoring, version control, reproducibility, and automation. This has important implications for testing. In the last two decades, a tremendous amount of work has been done on testing and building test suites.

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Organise your engineering teams around the work by reteaming

Abhishek Tiwari

The engineering organisation described may not work for you because of a team of 8-10 people is still a very big overhead. In this model, software architecture and code ownership is a reflection of the organisational model. Thirdly, let engineers themselves choose the delivery teams and organise them around the initiative.

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Educating a New Generation of Workers

O'Reilly

Entirely new paradigms rise quickly: cloud computing, data engineering, machine learning engineering, mobile development, and large language models. To further complicate things, topics like cloud computing, software operations, and even AI don’t fit nicely within a university IT department. What are people studying?

Education 108