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Multi-Modal Frontiers & Vector Architecture
How vector stores, multimodal embeddings, and efficient architectures power modern AI systems from edge to cloud.
Diese ausführliche Anleitung ist auf Englisch verfügbar.
10 Themen · 10 Anleitungen
- Real-Time Vector Streaming
Real-time vector streaming keeps embeddings and retrieval indices fresh as data changes, so search and RAG answers reflect the lat…
- GraphRAG and Knowledge Graphs
GraphRAG combines retrieval with a knowledge graph so answers can follow relationships, not just similarity. It excels on multi-ho…
- Cross-Modal Embedding Alignments
Cross-modal alignment maps text, images, audio, and other signals into a shared embedding space so one modality can retrieve anoth…
- Local SLMs in Production
Small language models run on modest hardware with low latency and predictable cost, making them attractive for edge and high-volum…
- Mixture of Experts (MoE)
Mixture of experts activates only a subset of parameters per token, delivering large-model quality at lower compute. The trade is …
- Continuous-Time AI Inference with SSMs
State space models handle long sequences with linear-time complexity and continuous-time dynamics, making them strong on streaming…
- AI Model Theft and Exfiltration
Model weights, embeddings, and training data are valuable intellectual property that can be stolen through APIs, insider access, o…
- Open-Source Visual Language Models
Open-source visual language models bring image understanding and generation to self-hosted stacks with control over data and cost.…
- Edge AI Orchestration
Edge AI orchestration coordinates models, data, and devices across distributed environments where connectivity and compute are lim…
- Energy-Efficient LLM Sampling
Sampling strategies and decoding choices affect not just output quality but energy and cost per token. Efficient sampling reduces …
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