Entities and Relationships in Database Design
Explore entity relationships, cardinality ratios, participation constraints, and structural notations in database design. Master key concepts for effective data modeling.
Getting Started with OpenSearch and OpenSearch Dashboards
Learn to set up and use OpenSearch for powerful search and analytics. Master cluster configuration, dashboard creation, and best practices for effective data visualization and management.
Spring AI: Integrating AI into Enterprise Applications with Java
Explore Spring AI's seamless integration of AI into enterprise Java applications. Learn key concepts, create effective prompts, and implement features like embeddings, RAG, and semantic search.
Democratizing AI - Vector Databases and LLM Applications
Explore vector databases, LLM app building, and AI democratization with Yujian Tang. Learn about Milvus, production use cases, data challenges, and the future of AI technologies.
Using Vector Databases: Practical Advice for Production - LLMs in Prod Conference
Explore vector databases for LLMs in production. Learn design patterns, context retrieval, feature injection, and common pitfalls. Gain practical insights for efficient deployment and management.
Embeddings and Retrieval for LLMs: Techniques and Challenges
Explore embedding-based retrieval for AI apps, including key limitations and mitigation techniques. Learn about Chroma, an open-source embedding database for LLM applications.
Model Blind Spot Discovery for Better Models and Ingesting Chaos - Mini Summit #2
Discover techniques for model blind spot detection and handling unstructured data at scale. Learn to build resilient AI models through data diagnosis, enhancement, and automated evaluation systems.
RAG Has Been Oversimplified - Exploring Complexities in Retrieval Augmented Generation
Explore the complexities of Retrieval Augmented Generation (RAG) beyond its oversimplified portrayal. Gain insights into practical challenges and nuanced considerations for developers working with RAG systems.
How Data Platforms Affect Machine Learning and Artificial Intelligence - MLOps Podcast
Explore how data platforms impact AI and ML applications, covering foundations, design patterns, data quality, and team structures for effective ML implementation.
Charting LLMOps Odyssey - Navigating Large Language Model Operations
Navigate the iterative development of LLM applications, addressing unique challenges in LLMOps design. Learn strategies for overcoming obstacles and adapting to the evolving landscape of large language model operations.
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.
Retrieval Augmented Generation - Techniques and Applications
Explore Retrieval Augmented Generation with Syed Asad. Dive into production issues, embedding models, inference layer experiments, and the evolving landscape of AI engineering in this insightful discussion.
Fresh Data and Smart Retrieval: Milvus and Jina CLIP Explained - MLOps Mini Summit 7
Explore vector database updates, Milvus mechanics, and Jina CLIP's novel multi-task contrastive training for enhanced multimodal and text-based retrieval systems. Master data freshness in high-throughput, low-latency scenarios.
Neo4J Graph Data Science: Centrality Algorithms
Explore centrality algorithms in Neo4J Graph Data Science Library for advanced network analysis and machine learning applications.
Neo4J GDS: Similarity Algorithms for Data Science and Machine Learning
Explore Neo4J GDS library's similarity algorithms for data science and machine learning, with practical demonstrations and code examples.
Neo4J GDS - Machine Learning Feature Engineering
Explore data pre-processing and feature engineering using Neo4J's Graph Data Science Library for enhanced machine learning workflows.
Neo4J GDS: Machine Learning Node Classification Pipelines
Crea pipelines de clasificación de nodos con Neo4J y Graph Data Science Library para aprender técnicas avanzadas de machine learning en grafos.
Neo4J as Vector Store with Langchain, OpenAI, and Wikipedia
Explore Neo4J as a vector store using Langchain, OpenAI embeddings, and Wikipedia data for advanced data science and machine learning applications.
MLOps MLflow: Installing a MLflow Tracking Server in Docker Containers
Install and configure MLflow Tracking Server with MariaDB backend using Docker containers for efficient MLOps workflows.
Neo4J Knowledge Graph: Topic Extraction with Langchain and Phi3 - Part 1
Explore topic extraction for knowledge graphs using Neo4J, Langchain, and Microsoft Phi3. Learn to enrich movie databases with themes from TMDB.org overviews.
Building Multi-Modal Search and RAG with Vector Databases
Explore open-source multimodal embeddings for cross-modal search and MM-RAG. Learn techniques for integrating diverse data types, enhancing decision-making with real-time processing and LLMs.