OpsMatters’ Post

The latest update for #Elastic includes "NLP vs. #LLMs: Understanding the differences" and "Elastic Universal #Profiling agent, a continuous profiling solution, is now #opensource". #Logging #Elasticsearch #DevOps https://lnkd.in/d3SsUnZ

The latest update for #Elastic includes "5 #AI #search trends impacting developers in 2024" and "Understanding AI search algorithms". #Logging #Elasticsearch #DevOps https://lnkd.in/d3SsUnZ

The latest update for #Elastic includes "Elastic and Red Hat: Accelerating public sector #AI and machine learning initiatives" and "Accelerating AI innovation: Introducing the Elastic AI Ecosystem". #Logging #Elasticsearch #DevOps https://lnkd.in/d3SsUnZ

The latest update for #Elastic includes "#Machinelearning vs. #AI: Understanding the differences" and "Emerging trends in #observability: GAI, #AIOps, tools consolidation, and #OpenTelemetry". #Logging #Elasticsearch #DevOps https://lnkd.in/d3SsUnZ

The latest update for #Elastic includes "IBM partners with #Elasticsearch to deliver Conversational #Search with watsonx Assistant" and "Beyond RAG basics: Advanced strategies for #AI applications". #Logging #DevOps https://lnkd.in/d3SsUnZ

The latest update for #Elastic includes "Beyond the trace: Pinpointing performance culprits with continuous #profiling and distributed tracing correlation" and "Elastic 8.13: GA of Amazon Bedrock in the Elastic AI Assistant for #Observability". #DevOps https://lnkd.in/d3SsUnZ

The latest update for #Elastic includes "Beyond RAG basics: Advanced strategies for \AI applications" and "#ElasticObservability 8.15: AI Assistant, OTel, and log quality enhancements". #Logging #Elasticsearch #DevOps https://lnkd.in/d3SsUnZ

The latest update for #Elastic includes "#ElasticObservability 8.15: AI Assistant, OTel, and log quality enhancements" and "#ElasticSearch 8.15: Accessible semantic #search with semantic text and reranking". #Logging #DevOps https://lnkd.in/d3SsUnZ

The latest update for #Elastic includes "#Elasticsearch achieves Certified Software Solution status for #MicrosoftAzure" and "Elastic and Red Hat: Accelerating #publicsector #AI and machine learning initiatives". #Logging #DevOps https://lnkd.in/d3SsUnZ

🌟 Day 11: #30DaysOfFLCode Challenge 🌟 Today, I explored matrix factorization and its integration with federated learning (FL)—a fascinating combination for decentralized recommendation systems! 🔍 What is Matrix Factorization? Matrix factorization is a technique used for building recommendation systems. It predicts user preferences by breaking down a sparse user-item matrix (e.g., users' movie ratings) into two smaller matrices: 1️⃣ User embeddings (capturing user preferences). 2️⃣ Item embeddings (representing the features of items). The dot product of these embeddings predicts user ratings, helping recommend unseen items. 🔗 Matrix Factorization in FL Traditional matrix factorization assumes centralized data, but FL lets us work on decentralized data across users' devices. Here’s how it works: Server: Stores the item matrix and aggregates updates. Clients: Locally train user embeddings and refine the item matrix using their data, without sharing sensitive info. This approach is stateless and reconstructs embeddings when needed, enabling scalability and handling unseen users effectively! 🔒 Privacy Budget in FL To ensure differential privacy, each client has a privacy budget—limiting how much data can be revealed over multiple queries. This prevents attackers from deducing sensitive information even if they collude or query repeatedly. 🚀 Plan for Tomorrow I aim to get hands-on with matrix factorization using FL, experimenting with implementation techniques and privacy-preserving strategies. Any tips or resources for implementing FL-based matrix factorization? Share them below! sources : https://lnkd.in/gTQTNSMq https://lnkd.in/g82Jh3Zu #FederatedLearning #MatrixFactorization #DifferentialPrivacy #AI