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How platform engineering and IDP observability can accelerate developer velocity

Dynatrace

Platform engineering creates and manages a shared infrastructure and set of tools, such as internal developer platforms (IDPs) , to enable software developers to build, deploy, and operate applications more efficiently. “That means making it available, resilient, and secure,” Grabner said.

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How Red Hat and Dynatrace intelligently automate your production environment

Dynatrace

Problem remediation is too time-consuming According to the DevOps Automation Pulse Survey 2023 , on average, a software engineer takes nine hours to remediate a problem within a production application. With that, Software engineers, SREs, and DevOps can define a broad automation and remediation mapping.

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Extend the AI and automation core of Dynatrace with host extensions to resolve infrastructure problems

Dynatrace

All the data bound to hosts is analyzed by the Davis AI causation engine and made available on custom dashboards and events pages. One of our software engineers, Tomasz Gajger, has been involved in a research project related to GPU performance analysis. Example 1: Gain visibility into your NVIDIA GPUs. What’s next.

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What is platform engineering?

Dynatrace

Platform engineering is a practice that outlines how development teams build internal platforms to create self-service capabilities for software engineering teams. The result is a cloud-native approach to software delivery. Platform engineering cannot stand alone, however.

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Unlock the Power of DevSecOps with Newly Released Kubernetes Experience for Platform Engineering

Dynatrace

Platform engineering is on the rise. According to leading analyst firm Gartner, “80% of software engineering organizations will establish platform teams as internal providers of reusable services, components, and tools for application delivery…” by 2026. Automation, automation, automation.

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Software engineering for machine learning: a case study

The Morning Paper

Software engineering for machine learning: a case study Amershi et al., More specifically, we’ll be looking at the results of an internal study with over 500 participants designed to figure out how product development and software engineering is changing at Microsoft with the rise of AI and ML. ICSE’19.

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Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and…

The Netflix TechBlog

Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and Efficiency By: Di Lin , Girish Lingappa , Jitender Aswani Imagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question?—?“Can