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Building High-Quality Software

DZone

It’s much better to build your process around quality checks than retrofit these checks into the existent process. NIST did classic research to show that catching bugs at the beginning of the development process could be more than ten times cheaper than if a bug reaches production. A side note.

Software 245
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Highlights from the O'Reilly Software Architecture Conference in New York 2018

O'Reilly Software

Watch highlights covering the latest tools and techniques of software architecture. From the O'Reilly Software Architecture Conference in New York 2018. Experts from across the software architecture world came together in New York for the O'Reilly Software Architecture Conference.

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Architecture & DDD Kata: Online Car Dealership

Strategic Tech

This kata is split into four sections that address different aspects of architecting software systems. All architecture decisions are ultimately driven by a company’s business model, so I think this is a sensible starting point. I’ve used it a few times now and it seems to have worked well so I thought it would be good to share.

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Enterprise Architecture in a Product-Oriented DevOps World

Strategic Tech

The traditional EA role of documenting business processes and capabilities serves a purposes. However, if nobody reads the documentation and it gets out of date quickly, it’s a tick-box exercise rather than a value creating one. It helps people to understand the complex systems they are working with.

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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. We heard many stories about difficulties related to data access and basic data processing. In many other frameworks, loading and storing of artifacts is left as an exercise for the user, which forces them to decide what should and should not be persisted.

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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. We heard many stories about difficulties related to data access and basic data processing. In many other frameworks, loading and storing of artifacts is left as an exercise for the user, which forces them to decide what should and should not be persisted.

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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. What does a modern technology stack for streamlined ML processes look like?

DevOps 138