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Modern ML inference often depends on up-to-date features (customer behaviour, session counts, recent events) that need to be available in low-latency operations. In this article you’ll learn how to build a real-time feature store on AWS using:
Introduction
In modern AI workflows, one common requirement is: given some piece of content (a document, image caption, query text), find “similar” items in your data store — not by exact keyword match, but by meaning. This is where vector embeddings + vector search come in. In this post we build a real API that:
Introduction
Vector databases are rapidly becoming a central element in AI workflows: storing embeddings (numeric vector representations of text, images or other data) and enabling semantic similarity search. In this post you’ll walk through a hands-on example of building a vector-db pipeline on Oracle Database 23 ai (or Autonomous/AI Database on Oracle Cloud Infrastructur...
In the age of generative AI and LLM-driven applications, one of the biggest challenges enterprises face is how to connect their business-critical data (structured and unstructured) to AI models in a performant, scalable and governed way. Enter vector databases and vector search: these allow you to represent unstructured data (documents, images, embeddings) as high-di...
Introduction
Cloud cost management isn’t just about checking invoices once a month — it’s about embedding automation, governance, and insights into your infrastructure so that your engineering teams make cost-aware decisions in real time. With OCI, you have native tools (Cost Analysis, Usage APIs, Budgets, etc.) and infras...
