Newton Omega Encoder — Time-Series Embeddings via /query

Omega is a time-series encoder: feed it a window of sensor readings, get back a fixed-size embedding you can do ML on. This skill calls the cloud Omega model on the same /query endpoint as the Newton fusion model — one stateless POST per window, embeddings back, no batch job or session lifecycle.

When to Apply

• Embed multivariate sensor windows (vibration, pressure, flow, network, …) into vectors
• Build lightweight downstream ML over those vectors client-side: KNN classification, anomaly scoring, similarity search, PCA/UMAP projection
• Prototype classification without standing up the managed batch pipeline

Do not use this skill when:

• The input is text, an image, or a video — that's the Newton fusion model (/query with Newton::c2_6_8b_fp8_...)
• You need fully-managed, server-side classification over millions of rows

For preparing the raw sensor CSVs (timestamp regularity, gap-aware blocks, temporal-order train/test split, the joint-state feature matrix), see atai-newton-omega-model-data-prep.

The Model