Data Software Architecture Software Engineering 
Dynatrace
MAY 23, 2024
The jobs executing such workloads are usually required to operate indefinitely on unbounded streams of continuous data and exhibit heterogeneous modes of failure as they run over long periods. Summary Ensuring fault tolerance in data-intensive, event-driven applications is crucial for successful industry deployments.
The Netflix TechBlog
DECEMBER 3, 2019
Netflix applies data science to hundreds of use cases across the company, including optimizing content delivery and video encoding. Data scientists at Netflix relish our culture that empowers them to work autonomously and use their judgment to solve problems independently. How could we improve the quality of life for data scientists?
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Dynatrace
MARCH 3, 2020
For software engineering teams, this demand means not only delivering new features faster but ensuring quality, performance, and scalability too. One way to apply improvements is transforming the way application performance engineering and testing is done. Here is a shortlist to get you started.
O'Reilly
OCTOBER 19, 2021
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. Why: Data Makes It Different. All ML projects are software projects. The new category is often called MLOps.
The Netflix TechBlog
DECEMBER 3, 2019
Netflix applies data science to hundreds of use cases across the company, including optimizing content delivery and video encoding. Data scientists at Netflix relish our culture that empowers them to work autonomously and use their judgment to solve problems independently. How could we improve the quality of life for data scientists?
O'Reilly Software
SEPTEMBER 7, 2018
From chaos architecture to event streaming to leading teams, the O'Reilly Software Architecture Conference offers a unique depth and breadth of content. We received more than 200 abstracts for talks for the 2018 O'Reilly Software Architecture Conference in London—on both expected and surprising topics.
O'Reilly
FEBRUARY 2, 2020
First, the behavior of an AI application depends on a model , which is built from source code and training data. A model isn’t source code, and it isn’t data; it’s an artifact built from the two. You need a repository for models and for the training data. Models almost certainly react to incoming data; that’s their point.
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