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10 tips for migrating from monolith to microservices

Dynatrace

However, the distributed system of a microservices architecture comes with its own cost: increased application complexity and convoluted testing. In fact, it can be difficult to make code changes that won’t disrupt the entire system. Use SLAs, SLOs, and SLIs as performance benchmarks for newly migrated microservices.

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How to evaluate modern APM solutions

Dynatrace

APM solutions track key software application performance metrics using monitoring software and telemetry data. Organizations use APM to ensure system availability, optimize service performance and response times, and improve user experiences. Artificial intelligence for IT operations (AIOps) for applications.

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What Is a Workload in Cloud Computing

Scalegrid

Simply put, it’s the set of computational tasks that cloud systems perform, such as hosting databases, enabling collaboration tools, or running compute-intensive algorithms. Such demanding use cases place a great value on systems capable of fast and reliable execution, a need that spans across various industry segments.

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Upcoming of the learned data structures

Abhishek Tiwari

Jeff is a Google Senior Fellow in the Google Brain team and widely known as a pioneer in artificial intelligence (AI) and deep learning community. This has far-reaching implications how future data systems and algorithms will be designed. Data structures are very foundational for the computer science and software engineering.

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Real-Real-World Programming with ChatGPT

O'Reilly

I really wanted to go beyond these quick gut reactions that I’ve seen so much of online, so I tried using ChatGPT for a few weeks to help me implement a hobby software project and took notes on what I found interesting. That’s because once the software environment has been set up (e.g., using “22” for the year 2022). and chrono-node.

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What We Learned Auditing Sophisticated AI for Bias

O'Reilly

A recently passed law in New York City requires audits for bias in AI-based hiring systems. AI systems fail frequently, and bias is often to blame. These examples of denigration and stereotyping are troubling and harmful, but what happens when the same types of systems are used in more sensitive applications? Data can be wrong.