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Could intellectual debt derail your journey to the autonomous cloud?

Dynatrace

Part two added a few simple examples of how intellectual debt might accrue, highlighting the subtle but real drag on efficiency. Since we began the series pointing out parallels to technical debt, let’s revisit that comparison. The reasons we inadvertently accrue intellectual debt are similar to those that contribute to technical debt.

Cloud 156
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Dynatrace achieves AWS Machine Learning Competency as it continues to enable a new generation of automated enterprise cloud

Dynatrace

This week Dynatrace achieved Amazon Web Services (AWS) Machine Learning Competency status in the new Applied Artificial Intelligence (Applied AI) category. Dynatrace news. The designation reflects AWS’ recognition that Dynatrace has demonstrated deep experience and proven customer success building AI-powered solutions on AWS.

AWS 144
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What is AIOps? A Software Intelligence Approach

Dynatrace

AIOps is the terminology that indicates the use of, typically, machine learning (ML) based artificial intelligence to cut through the noise in IT operations, specifically incident handling and management. Dynatrace news. These approaches are slow and inaccurate limiting its practical applications. Lost and rebuilt context.

Software 264
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Multi Cloud vs Hybrid Cloud Strategy

Scalegrid

Real-world examples like Spotify’s multi-cloud strategy for cost reduction and performance, and Netflix’s hybrid cloud setup for efficient content streaming and creation, illustrate the practical applications of each model. Thus making it an ideal choice for businesses seeking a successful implementation of their multi-cloud strategy.

Strategy 147
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The top four log analytics and log management best practices

Dynatrace

The resulting vast increase in data volume highlights the need for more efficient data handling solutions. Application performance monitoring (APM) , infrastructure monitoring, log management, and artificial intelligence for IT operations (AIOps) can all converge into a single, integrated approach.

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RPA Vs Test Automation

Testsigma

This is achieved through artificial intelligence and machine learning algorithms by learning the patterns from the user’s actions. RPA removes redundancy in the process and performs repetitive business tasks in order to achieve efficient and time-saving performance. Benefits of RPA. RPA vs Test Automation.

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Structural Evolutions in Data

O'Reilly

Hadoop’s value—being able to crunch large datasets—often paled in comparison to its costs. Their research has applied evolutionary algorithms to groups that need efficient ways to manage finite, time-based resources such as classrooms and factory equipment. And harder to sell a data-related product unless it spoke to Hadoop.