Remove Software Architecture Remove Software Engineering Remove Testing
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Up your quality and agility factor – using automation to build “performance-as-a-self-service”

Dynatrace

For software engineering teams, this demand means not only delivering new features faster but ensuring quality, performance, and scalability too. One way to apply improvements is transforming the way application performance engineering and testing is done. Check out Dynatrace’s Load testing tool integration.

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Nurturing Design in Your Software Engineering Culture

Strategic Tech

There are a few qualities that differentiate average from high performing software engineering organisations. I believe that attitude towards the design of code and architecture is one of them. When interviewing software engineers, I always provide at-home coding tests which focus on design.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. Software Architecture. This approach is not novel.

DevOps 145
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Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

The Netflix TechBlog

mainly because of mundane reasons related to software engineering. The infrastructure should allow them to exercise their freedom as data scientists but it should provide enough guardrails and scaffolding, so they don’t have to worry about software architecture too much.

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From Domains to Value Streams

Strategic Tech

The 2010s were a turning-point in the history of software engineering. or “How do software architecture, domains, Conway’s Law, Team Topologies, and value streams all fit together?”. Looking Ahead… Momentum is clearly building around the concept of value streams and the discipline of Value Stream Architecture.

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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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Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

The Netflix TechBlog

mainly because of mundane reasons related to software engineering. The infrastructure should allow them to exercise their freedom as data scientists but it should provide enough guardrails and scaffolding, so they don’t have to worry about software architecture too much.