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10 tips for migrating from monolith to microservices

Dynatrace

It is better to consider refactoring as part of the application transformation process before migrating, if possible. Use SLAs, SLOs, and SLIs as performance benchmarks for newly migrated microservices. Repeat this process throughout the different environments before development, staging, release, and production.

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Measuring the importance of data quality to causal AI success

Dynatrace

Improving data quality is a strategic process that involves all organizational members who create and use data. Additionally, teams should perform continuous audits to evaluate data against benchmarks and implement best practices for ensuring data quality. How can organizations improve data quality?

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How to evaluate modern APM solutions

Dynatrace

These solutions provide performance metrics for applications, with specific insights into the statistics, such as the number of transactions processed by the application or the response time to process such transactions. Artificial intelligence for IT operations (AIOps) for applications. Application performance insights.

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What Is a Workload in Cloud Computing

Scalegrid

In the realm of cloud-based business operations, there is an increasing dependence on complex information processing patterns. Utilizing cloud platforms is especially useful in areas like machine learning and artificial intelligence research. Ultimately improving efficiency while minimizing errors.

Cloud 130
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Real-Real-World Programming with ChatGPT

O'Reilly

And since this setup process was so new to me, I had a hard time thinking about how to phrase my questions. Perhaps future AI tool developers could use Swift Papers as a benchmark to assess how well their tool performs on an example real-real-world programming task. Right now, widely-used benchmarks for AI code generation (e.g.,

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What We Learned Auditing Sophisticated AI for Bias

O'Reilly

In particular, NIST’s SP1270 Towards a Standard for Identifying and Managing Bias in Artificial Intelligence , a resource associated with the draft AI RMF, is extremely useful in bias audits of newer and complex AI systems. Its operators have less experience and associated governance processes are less fleshed out.