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Google Cloud Next 2025 was a showcase of groundbreaking AI advancements. Native Multi-Agent Architecture: Build scalable applications by composing specialized agents in a hierarchy. BigFrames provides a Pythonic DataFrame and machinelearning (ML) API powered by the BigQuery engine. offers a scikit-learn-like API for ML.
At Gitex Global 2024, Core42, a leading provider of sovereign cloud and AI infrastructure under the G42 umbrella, signed a landmark agreement with semiconductor giant AMD. The partnership is set to trial cutting-edge AI and machinelearning solutions while exploring confidential compute technology for cloud deployments.
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AI and machinelearning are poised to drive innovation across multiple sectors, particularly government, healthcare, and finance. Data sovereignty and the development of local cloud infrastructure will remain top priorities in the region, driven by national strategies aimed at ensuring data security and compliance.
AI skills broadly include programming languages, database modeling, data analysis and visualization, machinelearning (ML), statistics, natural language processing (NLP), generative AI, and AI ethics. While many have performed this move, they still need professionals to stay on top of cloud services and manage large datasets.
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To address this consideration and enhance your use of batch inference, we’ve developed a scalable solution using AWS Lambda and Amazon DynamoDB. Conclusion In this post, we’ve introduced a scalable and efficient solution for automating batch inference jobs in Amazon Bedrock. This automatically deletes the deployed stack.
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Arrikto , a startup that wants to speed up the machinelearning development lifecycle by allowing engineers and data scientists to treat data like code, is coming out of stealth today and announcing a $10 million Series A round. “We make it super easy to set up end-to-end machinelearning pipelines. .
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AI practitioners and industry leaders discussed these trends, shared best practices, and provided real-world use cases during EXLs recent virtual event, AI in Action: Driving the Shift to Scalable AI. And its modular architecture distributes tasks across multiple agents in parallel, increasing the speed and scalability of migrations.
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Traditionally, building frontend and backend applications has required knowledge of web development frameworks and infrastructure management, which can be daunting for those with expertise primarily in data science and machinelearning. The full code of the demo is available in the GitHub repository.
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