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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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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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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. What: The Modern Stack of ML Infrastructure.

DevOps 138
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Tasktop Viz launch – DevOps Enterprise Summit 2019 – Day Two Recap

Tasktop

Scott Havens, Senior Director of Engineering at Mode Operandi, highlighted the benefits of event-based systems over legacy approaches, and how software architecture should be just as beautiful as the clothes on sale. Just look at how ugly that service-oriented architecture is!” I was like, “Wow! What’s next?

DevOps 8
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Amazon EC2 Cluster GPU Instances - All Things Distributed

All Things Distributed

These trade-offs have even impacted the way the lowest level building blocks in our computer architectures have been designed. Modern CPUs strongly favor lower latency of operations with clock cycles in the nanoseconds and we have built general purpose software architectures that can exploit these low latencies very well.Â

AWS 136