Building Recommendation Systems with Vector Search - Women of Search
Explore building recommendation systems using vector search, focusing on Ref2Vec for user representation and bipartite graphs for user-product connections. Learn to enhance e-commerce experiences through advanced search techniques.
Search and Retrieval - AI's Most Successful Hack
Explore hybrid search in RAG systems, combining metadata filtering and re-ranking for scalable LLM recommenders. Learn core concepts of retrieval, filtering, and ranking.
Personalizing Search Using Multimodal Latent Behavioral Embeddings
Explore personalized search using multimodal latent behavioral embeddings. Learn to integrate user behavior into modern search retrieval pipelines for RAG and end-user search, enhancing relevance and user experience.
Building Vector-Powered Products with User Embeddings
Explore vector embeddings for user modeling, learn vector ops, and discover applications in recommender systems and bot detection. Gain insights from real-world deployments.
Harnessing the Power of Data Science: Product Recommender System at Comparethemarket
Explore comparethemarket.com's journey in developing a product recommender system, from inception to production, enhancing customer experience and driving business growth.
Recommenders with Values: Developing Recommendation Engines in a Public Service Organization
Explore how BBC develops recommender systems aligned with public service values, addressing methodological and cultural challenges in content recommendation for diverse audiences.
10x the Retrieval Quality of Vector Search - Enhancing Semantic Search and RAG Systems
Enhance vector search retrieval quality by encoding diverse information into single vectors, simplifying deployment and reducing post-processing needs.
Unleashing the Full Potential of Your Recommender System in Production
Improve recommender systems through production monitoring, data insights, and drift detection to enhance performance and drive better business outcomes.
Practical Ways to Reduce Google Cloud Costs
Discover practical strategies to optimize and reduce Google Cloud expenses, including free tiers, recommenders, quotas, and auto-shutdown features.
Unlocking Innovative Generative AI Use Cases with Text and Multimodal Embeddings
Explore innovative generative AI applications using text and multimodal embeddings, focusing on semantic search, recommender systems, and content classification.
Real-Time Machine Learning: Features and Inference - MLOps Podcast #135
Explore real-time ML features and inference with LinkedIn experts. Gain insights on implementation, challenges, and best practices for moving from batch to interactive near real-time machine learning.
MLOps Build or Buy - Startup vs. Enterprise Perspectives
Explore MLOps strategies for startups and enterprises, focusing on build vs. buy decisions, data privacy, and recommender systems. Gain insights from Slack's ML engineers on platform development and integration challenges.
Clean Code for Data Scientists - MLOps Coffee Sessions
Explore clean coding practices for data scientists, Shopify's real-time serving platform Merlin, and insights on LLMs in production with Matt Sharp, Data Developer at Shopify.
Information Retrieval and Relevance: Vector Embeddings for Semantic Search
Explore vector embeddings for semantic search, learning techniques to create meaningful representations and apply them in information retrieval systems. Gain insights from an experienced technologist.
RecSys at Spotify - Building and Evaluating Large-Scale Recommender Systems
Explore Spotify's large-scale recommender systems, foundational embeddings for transfer learning, evaluation methods, and MLOps challenges with Senior ML Engineer Sanket Gupta.
From Robotics to Recommender Systems - MLOps Podcast #240
Explore robotics, computer vision, and recommender systems with Miguel Fierro. Gain insights on ML applications, sports analytics, and AI integration in various fields from a Microsoft Principal Data Science Manager.
Ray/RLLib-Empowered Reinforcement Learning Based Recommender Systems in NetEase Game
Explore how Ray/RLLib enhances reinforcement learning-based recommender systems in NetEase Games, improving user experience and profitability. Learn about the open-source RL4RS dataset for training and evaluating RL algorithms.
Large Language-Style Universal Models for Short Video Recommendations
Explore a ray-based solution for large-scale video recommendations, featuring a single big model with sparse inputs/outputs and universal graph search algorithm. Learn about its advantages and empirical results.
Large Scale Pathways Recommender Systems (PaRS) at Verizon
Explore Verizon's innovative multi-task learning recommender system, PaRS, leveraging Ray ecosystem to enhance customer experience through deep learning and efficient large-scale model training.
Improving Pareto Front Learning via Multi-Head HyperNetwork
Explore multi-objective optimization and Pareto front learning through a novel Multi-head HyperNetwork architecture, improving solution quality for machine learning applications.
Airflow's Limitations for MLOps - Challenges and Alternatives
Explore challenges in MLOps, data platforms, and Airflow with Stephen Bailey. Gain insights on modular job design, data classification, and practical approaches to data engineering and machine learning workflows.