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Nurturing Design in Your Software Engineering Culture

Strategic Tech

When I’ve worked with organisations deploying to production tens or hundreds of times per-day, it was this obsession on the small details that made the code and infrastructure easier to continuously improve and deploy. The same mindset should also be applied to architecture; involve the whole team and challenge the small details.

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A 5G future

O'Reilly

I’ve always been unimpressed by streaming services for music and video, at least partly because they’re least available when you most want them: when you’re flying or on a train, in at a technical conference with 3,000 attendees maxing out the hotel’s network. At a gigabit, you don’t have to think twice.

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

The Netflix TechBlog

About two years ago, we, at our newly formed Machine Learning Infrastructure team started asking our data scientists a question: “What is the hardest thing for you as a data scientist at Netflix?” Our job as a Machine Learning Infrastructure team would therefore not be mainly about enabling new technical feats.

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How architecture evolves into strategy

O'Reilly Software

It's a given that we must design a system, including a local software architecture, that actually runs, that is "solid." This document, the architecture definition , serves as the technologist's answer to the blueprint. Talent strategy: how you source and retain talent, how you train them. Solid doesn't mean inflexible.

Strategy 100
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MLOps and DevOps: Why Data Makes It Different

O'Reilly

As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. What: The Modern Stack of ML Infrastructure. Adapted from the book Effective Data Science Infrastructure. Foundational Infrastructure Layers.

DevOps 141
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Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

The Netflix TechBlog

About two years ago, we, at our newly formed Machine Learning Infrastructure team started asking our data scientists a question: “What is the hardest thing for you as a data scientist at Netflix?” Our job as a Machine Learning Infrastructure team would therefore not be mainly about enabling new technical feats.

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O’Reilly serverless survey 2019: Concerns, what works, and what to expect

O'Reilly

Respondents who have implemented serverless made custom tooling the top tool choice—implying that vendors’ tools may not fully address what organizations need to deploy and manage a serverless infrastructure. A related point: the rise of the serverless paradigm coincides with what we’ve referred to elsewhere as “ Next Architecture.”