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Building Conversational AI Applications (BCAIApps)

NVIDIA
8 Hours
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$500.00
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Description

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.

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