📌 Webinar: Improving search relevance with ML monitoring
Was this email forwarded to you? Sign up here 📌 Webinar: Improving search relevance with ML monitoringLet's take a dive into ML systems for ranking and search relevance and what it means to monitor them for quality, edge cases, and corrupt data
In today's fast-paced business environment, it's essential to have a search system that delivers high-quality results consistently. Machine learning systems for ranking and search relevance can help achieve this, but ongoing improvements are needed to ensure the system is accurate and effective. Join us at the webinar about how you can gain valuable insights, detect corrupt data, and deliver the best possible search experience for your users by tracking search results using tools on top of Elasticsearch and vector databases like Pinecone. April 19th, 1:00 PM ET 👇👇 You’re on the free list for TheSequence Scope and TheSequence Chat. For the full experience, become a paying subscriber to TheSequence Edge. Trusted by thousands of subscribers from the leading AI labs and universities.
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Big vs. Small, Open Source vs. API Based, the Philosophical Frictions of Foundation Models
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Sundays, The Sequence Scope brings a summary of the most important research papers, technology releases and VC funding deals in the artificial intelligence space.
📝 Guest Post: How to Enhance the Usefulness of Large Language Models*
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In this guest post, Filip Haltmayer, a Software Engineer at Zilliz, explains how LangChain and Milvus can enhance the usefulness of Large Language Models (LLMs) by allowing for the storage and
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Edge 281: Cross-Device Federated Learning
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📝 Guest Post: Caching LLM Queries for Improved Performance and Cost Savings*
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If you're looking for a way to improve the performance of your large language model (LLM) application while reducing costs, consider utilizing a semantic cache to store LLM responses. By caching
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