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Dynatrace Perform 2024 Guide: Deriving business value from AI data analysis

Dynatrace

AI-enabled chatbots can help service teams triage customer issues more efficiently. Deriving business value with AI, IT automation, and data reliability When it comes to increasing business efficiency, boosting productivity, and speeding innovation, artificial intelligence takes center stage. What is explainable AI?

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Implementing AWS well-architected pillars with automated workflows

Dynatrace

This is a set of best practices and guidelines that help you design and operate reliable, secure, efficient, cost-effective, and sustainable systems in the cloud. The framework comprises six pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability.

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Web Development Trends in 2023

KeyCDN

Chatbots and virtual assistants Chatbots and virtual assistants are becoming more common on websites and web applications as they provide an efficient and convenient way for users to interact with a business. The main benefits of serverless architecture are cost savings and scalability.

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Sustainability Talks and Updates from AWS re:Invent 2023

Adrian Cockcroft

The Pantheon in Rome — Extremely sustainable architecture — photo by Adrian I wrote a medium post after AWS re:Invent 2022 summarizing the (lack of) news and all the talks related to Sustainability. This includes providing the efficient, resilient services AWS customers expect, while minimizing their environmental footprint.

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Sustainability at AWS re:Invent 2022 All the talks and videos I could find…

Adrian Cockcroft

The keynotes didn’t feature anything new on carbon, just re-iterated the existing path to 100% green energy by 2025. We also may choose to support these grids through the purchase of environmental attributes, like Renewable Energy Certificates and Guarantees of Origin, in line with our Renewable Energy Methodology.

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A case for managed and model-less inference serving

The Morning Paper

.” If we want an increasing number of applications to use machine learning, we must automate issues that affect ease-of-use, performance, and cost efficiency for users and providers… Despite significant research, this is missing right now. We’d like to pack models as efficiently as possible on the underlying infrastructure.