Learn how to run end-to-end conversational voice systems directly as a SageMaker endpoint

🎥 On-Demand Webinar

Speakers:

Evan Henry — Director of Product, AI Infrastructure and Cloud Partnerships

Dan Ferguson — SageMaker Solutions Architect, AWS

Until now, deploying real-time voice AI inside SageMaker required stitching together custom WebSockets, Lambda functions, and infrastructure glue that slowed teams down and introduced brittle scaling patterns.

With SageMaker’s new bidirectional streaming and Deepgram’s real-time STT, TTS, and Voice Agent APIs, you can now run end-to-end conversational voice systems directly as a SageMaker endpoint — no custom streaming stack required.

Watch this on-demand technical session to get a deep walkthrough of how streaming audio flows through a SageMaker real-time endpoint, how Deepgram’s models operate inside the bidirectional channel, and how to build low-latency, production-ready voice pipelines with AWS-native security, scaling, and compliance.

You'll walk away with a full blueprint for architecting real-time speech downstream agents, integrating LLM logic, and deploying voice-intensive workloads securely inside your AWS environment.

• How SageMaker Bidirectional Streaming Works: Get a detailed breakdown of real-time inference APIs and how audio flows through persistent, low-latency channels.
• Deploying Real-Time STT & TTS Inside SageMaker: Learn how Deepgram’s streaming STT and generative TTS models run natively as SageMaker endpoints — without maintaining your own infrastructure.
• Building Voice Pipelines with AWS-Native Controls: See how enterprises use VPC, IAM, and SageMaker scaling to run voice workloads securely and cost-effectively.
• Blueprint for Voice Agent Architectures: Explore how to combine Deepgram STT/TTS with LLMs inside SageMaker to power live, multi-turn conversational systems.

Watch on-demand now to see how Deepgram and AWS are shaping the next generation of real-time voice AI — built directly into SageMaker. Register above.