As our world continues to evolve and become more digital, conversational AI is increasingly used to facilitate human-to-machine communication. Conversational AI is the technology that powers automated messaging and speech-enabled applications, and its applications are used in various industries to improve overall customer experience, while improving customer service efficiency.
Conversational AI pipelines are complex and expensive to develop from scratch. In this course, you'll learn how to build a conversational AI service using the NVIDIA Riva framework. Riva provides a complete, GPU-accelerated software stack, making it easy for developers to quickly create, deploy, and run end-to-end, real-time conversational AI applications that can understand terminology that’s unique to each company and its customers. The Riva framework includes pretrained conversational AI models, tools, and optimized services for speech, vision, and natural language understanding (NLU) tasks. With Riva, developers can create customized language-based AI services for intelligent virtual assistants, virtual customer service agents, real-time transcription, multi-user diarization, chatbots, and much more.
In this workshop, you’ll learn how to quickly build and deploy production quality conversational AI applications with real-time transcription and natural language processing (NLP) capabilities. You’ll integrate NVIDIA Riva automatic speech recognition (ASR) and named entity recognition (NER) models with a web-based application to produce transcriptions of audio inputs with highlighted relevant text. You'll then customize the NER model, using NVIDIA TAO Toolkit to provide different targeted highlights for the application. Finally, you'll explore the production-level deployment performance and scaling considerations of Riva services with Helm Charts and Kubernetes clusters.
Learning Objectives
By participating in this workshop, you’ll learn:
- How to deploy and enable pretrained ASR and NER models on Riva for a conversational AI application.
- How to fine-tune and deploy domain-specific models with TAO Toolkit.
- How to deploy a production-level conversational AI application with a Helm Chart for scaling in Kubernetes clusters.
Download workshop datasheet (PDF 318 KB)