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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

Organizations use APM to ensure system availability, optimize service performance and response times, and improve user experiences. ” APM vendors originally designed their solutions to quickly identify application performance issues in monolithic on-premises apps. APM solutions: A primer. Application performance insights.

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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.

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Real-Real-World Programming with ChatGPT

O'Reilly

Here’s how I worked on it: I subscribed to ChatGPT Plus and used the GPT-4 model in ChatGPT (first the May 12, 2023 version, then the May 24 version) to help me with design and implementation. I liked how ChatGPT helped me work through the tradeoffs of these initial design decisions before diving head-first into coding.

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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.