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MLOps Experts Are Moving to Data-Centric Workflows and You Should Too

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Imagine this scenario: Your company has invested significantly in AI models designed to detect fraud, improve customer interactions, or anticipate maintenance needs. Initially, the excitement is palpable, with promises of transformative business outcomes. But soon enough, things slow down. Your models are making decisions, but why those decisions are made, and whether they’re truly optimal, […]

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Current London 2025: Unleashing Event-Driven AI with Kafka and Seldon Core 2

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Remember when streaming data felt like the next big thing but the market wasn’t quite ready for the actual complexity of AI? Current London 2025 (formerly Kafka Summit) made it clear that event-driven AI is no longer an idea for tomorrow as it’s already transforming how organizations operate today. Kafka is central to this evolution

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Introducing Seldon Core 2.9 and MLServer 1.7: Enhanced Autoscaling, Streaming, and Usability

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May we introduce you to our newest update, Seldon Core 2.9! This release is packed with updates that make deploying and scaling ML easier and more flexible than ever, including improvements to autoscaling, support for streamed results for GenAI models, and various other usability improvements described in more detail below. Also tested alongside a new

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Embracing the AI-Driven Workplace: Reflections on the D8Ai Panel Discussion

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Yesterday, I had the privilege of speaking on a panel hosted by the D8Ai Club at Rise in London, where we dove into the powerful (and rapid) shift that AI is driving in the modern workplace. While some claim the AI revolution is coming, the reality is that it’s already here and evolving faster than

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Getting Started with Machine Learning Monitoring

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Machine learning models are powerful tools when used to automate processes and inform data-led decisions. But the effectiveness of models can degrade if left unmonitored and unoptimized. The lifecycle of a machine learning model should include constant tweaks and improvements to maintain and improve accuracy and efficiency. Without a process of machine learning monitoring, this

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Predicting Customer Demand With Machine Learning

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Demand is a key indicator of the operational and expansion prospects for retail organizations, and being able to forecast this can be the difference between retailers surviving and thriving in a competitive landscape. The most critical business factors, such as revenue, profit margins, capital expenditure, supply chain management etc., are directly dependent on demand.  

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