Vector Databases in Action: Scalable Retrieval for AI, RAG, and Agent Memory Systems (The Automation Stack)
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Fuera de stock
Fuera de stock
Peso con empaque:
0.33 kg
0.33 kg
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No
No
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Nuevo
Nuevo
Producto de:
Amazon
Amazon
Sobre este producto
- Fundamentals of Vector Representations – Understand embeddings, similarity metrics, and dimensionality reduction.
- Indexing and Nearest-Neighbor Search – Master HNSW, IVF, and other ANN structures for blazing-fast lookups.
- Architectures of Vector Databases – Explore storage engines, sharding, and deployment strategies (cloud vs. open-source).
- Getting Started with Leading Vector DBs – Step-by-step walkthroughs for Pinecone, Weaviate, Milvus, Qdrant, and Chroma.
- Integrating with LLMs and RAG Workflows – Build retrieval pipelines that feed context into large language models for accuracy and coherence.
- Vector Databases as Agent Memory – Design short- and long-term memory stores so your bots recall user preferences across sessions.
- Multi-Modal and Advanced Use Cases – Implement image/audio search, real-time recommendations, and anomaly detection with vectors.
- Performance Tuning and Monitoring – Benchmark, fine-tune parameters, and set up alerts to maintain SLAs.
- Security, Privacy, and Access Control – Apply encryption, anonymization, and audit logging to protect sensitive data.
- Production Patterns and Deployment – Automate schema changes, use blue-green releases, and craft disaster recovery plans.
- Case Studies – See semantic search at scale, agent-driven support, e-commerce recommendations, and live anomaly monitoring in action.
- Future Directions – Discover federated indexes, on-device search, and self-tuning architectures powered by reinforcement learning.
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