SKU/Artículo: AMZ-B0FRSZXTNB

Embedding-Based Retrieval in Action: Real-World AI Applications with Semantic Search and RAG 2.0 for RAG Workflows, and Vector Databases

Disponibilidad:
Fuera de stock

Peso con empaque:
0.33 kg

Devolución:
No

Condición
Nuevo

Producto de:
Amazon

Sobre este producto

Foundations of Embeddings and Retrieval – how AI moved from keyword search to dense vectors.
Semantic Search Explained – principles, limitations of traditional search, and similarity metrics.
Introduction to RAG and RAG 2.0 – why grounding matters and how RAG 2.0 enhances reliability.
Working with Vector Databases – Pinecone, Weaviate, Milvus, FAISS, schema design, and performance.
Building Retrieval Pipelines – ingestion, querying, ranking, and evaluating quality.
Embeddings in Practice – generating, fine-tuning, and balancing pretrained vs. custom models.
RAG 2.0 in Real Applications – chatbots, enterprise assistants, and recommendation systems.
Scaling and Deployment – distributed systems, Kubernetes, serverless strategies, and cost optimization.
Evaluation, Security, and Compliance – benchmarks, reducing hallucinations, explainability, and privacy.
Future Directions – multimodal retrieval, emerging trends in vector databases, and the role of embeddings in agentic AI.