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

The Netflix TechBlog

by David Berg , Ravi Kiran Chirravuri , Romain Cledat , Savin Goyal , Ferras Hamad , Ville Tuulos tl;dr Metaflow is now open-source! 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?”

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

The Netflix TechBlog

by David Berg , Ravi Kiran Chirravuri , Romain Cledat , Savin Goyal , Ferras Hamad , Ville Tuulos tl;dr Metaflow is now open-source! 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?”

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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing One approach to such a challenging scenario is stream processing, a computing paradigm and software architectural style for data-intensive software systems that emerged to cope with requirements for near real-time processing of massive amounts of data.

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What is distributed tracing and why does it matter?

Dynatrace

Distributed tracing follows an interaction by tagging it with a unique identifier, which stays with it as it interacts with microservices, containers, and infrastructure. It can also offer real-time visibility into user experience, from the top of the stack right down to the application layer and the large-scale infrastructure beneath.

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What is distributed tracing and why does it matter?

Dynatrace

Distributed tracing follows an interaction by tagging it with a unique identifier, which stays with it as it interacts with microservices, containers, and infrastructure. It can also offer real-time visibility into user experience, from the top of the stack right down to the application layer and the large-scale infrastructure beneath.

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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 137
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Connect Fluentd logs with Dynatrace traces, metrics, and topology data to enhance Kubernetes observability

Dynatrace

Fluentd is an open-source data collector that unifies log collection, processing, and consumption. Detailed performance analysis for better software architecture and resource allocation. Dynatrace news. What is Fluentd? It collects, processes, and outputs log files to and from a wide variety of technologies.

Metrics 188