Simplify your AI workflow with autonomous embedding generation in BigQuery

Simplify your AI workflow with autonomous embedding generation in BigQuery

• Simplify your AI workflow with autonomous embedding generation in BigQuery Software Engineer Software Engineer Our most intelligent model available yet for complex tasks on Gemin

Nemotron ColEmbed V2: Raising the Bar for Multimodal Retrieval with ViDoRe V3's Top Model

Nemotron ColEmbed V2: Raising the Bar for Multimodal Retrieval with ViDoRe V3's Top Model

• Nemotron ColEmbed V2 introduces late‑interaction embeddings for unified text‑image retrieval. • Three model sizes-3B, 4B, 8B-deliver state‑of‑the‑art accuracy on ViDoRe V1‑V3. •

Powering Vector Embedding Capabilities

Powering Vector Embedding Capabilities

• Expedia Group Technology - Data Science Powering Vector Embedding Capabilities Empowering developers with seamless vector embedding solutions Introduction Rapid advances in Machi

Engineering Blogs · January 6, 2026 (updated February 24, 2026) · 2 min · 264 words
Token-count-based Batching: Faster, Cheaper Embedding Inference for Queries

Token-count-based Batching: Faster, Cheaper Embedding Inference for Queries

• Token-count-based Batching: Faster, Cheaper Embedding Inference for Queries Embedding model inference often struggles with efficiency when serving large volumes of short requests