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Development

Featured in Development

Designing for Concurrency: the Hilbert’s Hotel Problem in Go

In this article, we want to show how achieving concurrency is the result of an appropriate design. A concurrent solution may turn out to be more elegant and easier to reason about than an equivalent sequential algorithm. To illustrate these concepts we use, as an example, the Hilbert’s Hotel mathematical problem.

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Architecture & Design

Featured in Architecture & Design

Expanding GraphQL Federation at Netflix with Tejas Shikhare

GraphQL can be a great choice for client to server communication, but it requires investment to maximize its potential. Netflix operates a very large, Federated GraphQL platform. Like any distributed system, this has some benefits, but also creates additional challenges. In this episode, Tejas Shikhare explains the pros and cons of scaling GraphQL adoption.

All in architecture-design

AI, ML & Data Engineering

Featured in AI, ML & Data Engineering

Resilient Real-Time Data Streaming across the Edge and Hybrid Cloud

Kai Waehner explores different architectures and their trade-offs for transactional and analytical workloads. Real-world examples include financial services, retail, and the automotive industry.

All in ai-ml-data-eng

Culture & Methods

Featured in Culture & Methods

Sustaining Fast Flow with Socio-Technical Thinking

Nick Tune shares principles and practices from the fields of DDD and Team Topologies that leaders can apply to create high-performing teams and sustainable flow throughout their organization.

All in culture-methods

DevOps

Featured in DevOps

If Twitter Doesn’t Have a Staging Environment, Should Anyone?

With Twitter revealing they do not have a staging environment, how important is staging to delivering quality? When so many tasks are shifting to the developer these days and no one best developer approach fits all, how do dev teams determine the best developer methodology for them and their company?

All in devops

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AI, ML & Data Engineering

AI, ML and Data Engineering InfoQ Trends Report

How AI, ML and Data Engineering evolved in 2021 as seen by the InfoQ editorial team. Topics discussed include deep learning, edge deployment of machine learning algorithms, commercial robot platforms, GPU and CUDA programming, natural language processing and GPT-3, MLOps, and AutoML.

Kimberly McGuire Srini Penchikala Rags Srinivas Anthony Alford
on Jan 03, 2022

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58:59
AI, ML & Data Engineering

AI, ML and Data Engineering InfoQ Trends Report - August 2021

How AI, ML and Data Engineering are evolving in 2021 as seen by the InfoQ editorial team. Topics discussed include deep learning, edge deployment of machine learning algorithms, commercial robot platforms, GPU and CUDA programming, natural language processing and GPT-3, MLOps, and AutoML.

Kimberly McGuire Srini Penchikala Rags Srinivas Anthony Alford
on Aug 09, 2021

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59:14
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