When Unlearning Is Free: Leveraging Low Influence Points to Reduce Computational Costs

As concerns around data privacy in machine learning grow, the ability to unlearn—or remove—specific data points from trained models becomes increasingly important. While state-of-the-art unlearning methods have emerged in response, they typically treat all points in the forget set equally. In this work, we challenge this approach by asking: do points that have a negligible …

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In-House LLM Serving at Netflix

By AI Platform’s Model Runtime team and Inference team Introduction Most organizations consume LLMs through hosted APIs. Netflix went further — we run the full stack ourselves, from model deployment through inference, inside our existing production environment rather than a separate ML silo. Some of those decisions weren’t obvious, and a few revealed their trade-offs only under …

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Transform your sales organization with Amazon Quick: your new agentic AI teammate

The average sales rep spends only 40% of their time actually selling. The rest is eaten up by necessary, but lower-value work: customer relationship management (CRM) updates, prospect research, email drafting, and endless context-switching between tools. Before you know it, the day has passed and you’ve spent only a small fraction of your time on …

13 hands-on demos to build on Gemini Enterprise Agent Platform

Earlier this year, we introduced Gemini Enterprise Agent Platform, where you can build, scale, govern, and optimize agents. Today, we’re sharing 13 demos that walk you through what Agent Platform can do. Each one teaches a concept, a pattern, or an architecture you can put to work immediately. The best part? You don’t have to …