Building Conversational AI Applications (BCAA)
- Course Code GK847011
- Duration 1 day
Course Delivery
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Course Delivery
This course is available in the following formats:
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Company Event
Event at company
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Virtual Learning
Learning that is virtual
Request this course in a different delivery format.
Course Overview
TopBuild and deploy a conversational AI pipeline including transcription, NLP, and speech.
You'll explore automatic speech recognition (ASR) and text-to-speech (TTS) models and their customization in detail with the NVIDIA NeMo framework and learn how to deploy the models with Riva. Finally, you'll explore the production-level deployment performance and scaling considerations of Riva services with Helm charts and Kubernetes clusters.
Virtual Learning
This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.
Course Schedule
TopCourse Objectives
Top- How to customize and deploy ASR and TTS models on Riva.
- How to build and deploy an end-to-end conversational AI pipeline, including ASR, NLP, and TTS models, on Riva.
- How to deploy a production-level conversational AI application with a Helm chart for scaling in Kubernetes clusters.
Course Content
TopModule 1: Introduction
- Meet the instructor.
- Create an account.
Module 2: Introduction to Conversational AI
- Work through an ASR model example from audio to spectrogram to text.
- Explore decoders, customizations, and additional models, including inverse text normalization (ITN), punctuation and capitalization, and language identification.
- Deploy Riva ASR.
Module 3: Inference and Deployment Challenges
- 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.
Module 4: Final Review
- Review key learnings and answer questions.
- Complete the assessment and earn a certificate.
- Complete the workshop survey.
- Learn how to set up your own AI application development environment.
Course Prerequisites
Top- Basic Python programming experience
- Fundamental understanding of a deep learning framework, such as TensorFlow, PyTorch, or Keras
- Basic understanding of neural networks
Follow on Courses
Top- Get Started with Highly Accurate Custom ASR for Speech AI
- Building Transformer-Based Natural Language Processing Applications
- Accelerating Data Engineering Pipelines
