MySQL for AI Applications: Vector Storage, JSON, and ML Integration
Comprehensive guide to using MySQL for AI workloads including vector embeddings, JSON document storage, ML model management, and production AI pipelines.
Comprehensive guide to using MySQL for AI workloads including vector embeddings, JSON document storage, ML model management, and production AI pipelines.
Discover how MySQL powers production systems: web applications, e-commerce, CMS, logging, analytics, and multi-tenant SaaS with practical examples.
Deep dive into MySQL architecture. Understand InnoDB storage engine, buffer pool, MVCC, query execution, and transaction management internals.
Learn MySQL administration: backup strategies, point-in-time recovery, replication, MySQL InnoDB Cluster, ProxySQL, and production monitoring.
Master MySQL from installation to advanced queries. Learn data types, constraints, indexes, and SQL operations with practical examples.
Learn the fundamentals of Neo4j including nodes, relationships, labels, properties, and Cypher query language for graph data modeling.
Leverage Neo4j for AI applications including knowledge graph construction, vector embeddings, GraphRAG pipelines, and machine learning feature engineering.
Deep dive into Neo4j architecture: storage engine, property files, relationship traversal, indexes, caching, and query execution pipeline.
Master Neo4j operations including installation, configuration, backup, recovery, monitoring, clustering, and production best practices.
Explore the latest Neo4j developments including version 5.x features, GraphRAG, multi-database support, graph machine learning, and the evolving graph ecosystem.