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The key to operational AI: Modern data architecture

CIO

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

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The AI Future According to Google Cloud Next ’25: My Interesting Finds

Xebia

Native Multi-Agent Architecture: Build scalable applications by composing specialized agents in a hierarchy. ADK powers the newly announced Agentspace, Google’s research agent and Google customer support agents. Take a look at the Agent Garden for some examples! BigFrames 2.0 offers a scikit-learn-like API for ML.

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Reimagine application modernisation with the power of generative AI

CIO

According to research from NTT DATA , 90% of organisations acknowledge that outdated infrastructure severely curtails their capacity to integrate cutting-edge technologies, including GenAI, negatively impacts their business agility, and limits their ability to innovate. [1]

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Microservice Architecture of Alibaba

Abhishek Tiwari

Alibaba has constructed a sophisticated microservices architecture to address the challenges of serving its vast user base and handling complex business operations. This article draws from research by Luo et al.,

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12 AI predictions for 2025

CIO

According to a Bank of America survey of global research analysts and strategists released in September, 2024 was the year of ROI determination, and 2025 will be the year of enterprise AI adoption. Agents can be more loosely coupled than services, making these architectures more flexible, resilient and smart.

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Determining the best architecture for your CX platform

CIO

Without the right cloud architecture, enterprises can be crushed under a mass of operational disruption that impedes their digital transformation. What’s getting in the way of transformation journeys for enterprises? Unfortunately, research shows otherwise.

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Extend large language models powered by Amazon SageMaker AI using Model Context Protocol

AWS Machine Learning - AI

We will deep dive into the MCP architecture later in this post. For MCP implementation, you need a scalable infrastructure to host these servers and an infrastructure to host the large language model (LLM), which will perform actions with the tools implemented by the MCP server. The following diagram illustrates this workflow.