Join our session on minimizing latency in self-hosted #ML models in cloud environments. Learn strategies for deploying Deepgram’s speech-to-text models on your hardware, including concurrency limits, auto-scaling, input chunk granularity, and efficient model loading. Optimize your ML inference.
Julia Kroll is an Applied Engineer at Deepgram where she provides engineering and product expertise on speech-to-text and voice AI, enabling developers to use language as the universal interface between humans and machines. She previously worked as a Senior Machine Learning Engineer creating natural-sounding AI voices, following five years at Amazon, where she contributed to machine learning and data engineering for AWS and Alexa. She holds two computer science degrees, a master's from the University of Wisconsin-Madison and a bachelor's from Carleton College. Her interests lie at the intersection of technology, linguistics, and society.
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