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AI for everyone - How companies can benefit from the advance of machine learning

All Things Distributed

For the past 20 years, thousands of software engineers at Amazon have been working on ML. For example programming interfaces that developers can use to analyze images, change text into true-to-life language or create chatbots. Decisive is the openness of the layers and the reliable availability of the infrastructure.

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World’s Top Web Performance Leaders To Watch

Rigor

Rick is a software engineer on the Google Chrome team, “leading an effort to make the web just work for developers.” Sergey is a principal engineer at Meetup and a well-known performance educator who regularly runs monthly hands-on Meet4SPEED events in New York City. Rick Byers. Rick Byers. Sergey Chernyshev.

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Scaling Appsec at Netflix (Part 2)

The Netflix TechBlog

including bug bounty, pentesting, PSIRT (product security incident response), security reviews, and developer security education?—?via The partnership charter has also grown laterally into the infrastructure space as we stack our leverage bets on infrastructure components (like Service Mesh, Container Platform, etc).

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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. In effect, the engineer designs and builds the world wherein the software operates. The new category is often called MLOps.

DevOps 138
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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. As noted earlier, the majority of survey respondents are software engineers. Industries of survey respondents.

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Experimentation is a major focus of Data Science across Netflix

The Netflix TechBlog

A key aspect of a data scientist’s role in these analyses is to educate stakeholders on the caveats that come with these studies, while still providing rigorous evaluation and actionable insights, and providing structure to some otherwise ambiguous problems. without having to become software engineers themselves.