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 Jarvis framework. Jarvis 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 Jarvis framework includes pretrained conversational AI models, tools, and optimized services for speech, vision, and natural language understanding (NLU) tasks. With Jarvis, 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 Jarvis 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 Transfer Learning Toolkit (TLT) to provide different targeted highlights for the application. Finally, you’ll explore the production-level deployment performance and scalingconsiderations of Jarvis services with Helm Charts and Kubernetes clusters.
Building Conversational AI Applications (BCAIApps)
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Course Summary
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Description
Objectives
In this workshop, you will learn:
- How to deploy and enable pretrained ASR and NER models on Jarvis for a conversational AI application.
- How to fine-tune and deploy domain-specific models with TLT.
- How to deploy a production-level conversational AI application with a Helm Chart for scaling in Kubernetes clusters.
Prerequisites
Prerequisites:
- Basic Python programming experience
- Fundamental understanding of a deep learning framework, such as TensorFlow, PyTorch, or Keras
- Basic understanding of neural networks
Outline
Introduction (15 mins)
- Meet the instructor.
- Create an account at courses.nvidia.com/join
Introduction to Conversational AI (120 mins)
Explore the conversational AI landscape and gain a deeper understanding of the key components of ASR and NLP pipelines
- Work through a TLT model inference example with speech recognition
- Deploy Jarvis ASR and NER models >Launch a contact application with ASR and NER
Model Customizations (120 mins)
Explore the details of Jarvis architecture and discuss the workflow involved in deployment of fine-tuned models using TLT.
- Fine-tune NER for a specific domain
- Deploy a customized NER model within Jarvis
- Launch the application with updated models
Inference and Deployment Challenges(120 mins)
Explore challenges related to performance, optimization, and scaling in production deployment of conversational AI applications
- Gain an understanding of the inference deployment process
- Analyze non-functional requirements and their implications
- Use a Helm Chart to deploy a conversational AI application with a Kubernetes cluster
Final Review (15 mins)
- Review key objectives and answer questions
- Finish the assessment and earn a certificate
- Complete the workshop survey
- Learn how to set up your own AI application development environmentNext Steps Continue learning with these DLI trainings:
- Fundamentals of Deep Learning
- Building Transformer-Based Natural Language Processing Applications
- Accelerating Data Engineering Pipelines