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Why digital transformation hinges on SRE teams

Dynatrace

Site reliability engineering seeks to bridge the gap between developers and operations teams, embedding reliability and resiliency into each stage of the software development lifecycle. Service-level objectives (SLOs) are key to the SRE role; they are agreed-upon performance benchmarks that represent the health of an application or service.

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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. It explains that APM tools “allow users to monitor and track the performance of particular software or web applications to identify and solve any performance issues that may arise. APM solutions: A primer.

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10 tips for migrating from monolith to microservices

Dynatrace

Security should be an integral part of each stage of the software delivery lifecycle, from development to monitoring in real time. Use SLAs, SLOs, and SLIs as performance benchmarks for newly migrated microservices. This includes when teams refactor applications for microservices architecture.

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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly

Most teams approach this like traditional software development but quickly discover it’s a fundamentally different beast. Check out the graph belowsee how excitement for traditional software builds steadily while GenAI starts with a flashy demo and then hits a wall of challenges? Whats worse: Inputs are rarely exactly the same.

Systems 67
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What Is a Workload in Cloud Computing

Scalegrid

Utilizing cloud platforms is especially useful in areas like machine learning and artificial intelligence research. This includes zero-day vulnerabilities and software weaknesses that are not yet known and can be exploited without warning. What is an example of a workload?

Cloud 130
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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. The benchmarking was performed using 3 real-world data sets (weblogs, maps, and web-documents), and 1 synthetic dataset (lognormal).

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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. For audit results to be recognized, audits have to be transparent and fair.