Browsing: scale

Production machine learning systems rarely consume raw data directly. Before a model can make a prediction, its feature-serving system may need to read stored signals, filter events to a time window, count or sum values, normalize them, and combine intermediate results.

As voice becomes a first-class modality for AI, Deepgram’s models span speech-to-text, text-to-speech, and voice agents. They now process quadrillions of audio tokens a year for more than 200,000 developers and 1,400 organizations. Customers use them through the Deepgram API or run them in their own environments, including on Amazon SageMaker AI.