Videos and demos
PDF of this doc site
- AI Converged Infrastructures
Data Pipelines, Data Lakes and Management
- NetApp AI Control Plane
- Distributed training in Azure - Click-Through Rate Prediction
- Conversational AI using NVIDIA
- Anthos with NetApp
- DevOps with NetApp Astra
Red Hat OpenShift with NetApp
- NetApp Storage Integrations Overview
Solution Validation and Use Cases
- Red Hat OpenShift Virtualization with NetApp ONTAP
- VMware Tanzu with NetApp
- Data Migration and Data Protection
- Oracle Database
- SnapCenter for Databases
- Modern Data Analytics
NetApp Hybrid Multicloud with VMware
- VMware for Public Cloud
- NetApp for GCP / GCVE
- NetApp Hybrid Multicloud with Red Hat OpenShift
- VMware Virtualization for ONTAP
- Virtual Desktops
- Artificial Intelligence
There are two notebooks that contain the sentiment analysis pipeline: “Support-Center-Model-Transfer-Learning-and-Fine-Tuning.ipynb” and “Support-Center-Sentiment-Analysis-Pipeline.ipynb”. Together, these notebooks demonstrate how to develop a pipeline to ingest support center data and extract sentiments from each sentence using state-of-the-art deep learning models fine-tuned on the user’s data.
Support Center - Sentiment Analysis Pipeline.ipynb
This notebook contains the inference RIVA pipeline for ingesting audio, converting it to text, and extracting sentiments for use in an external dashboard. Dataset are automatically downloaded and processed if this has not already been done. The first section in the notebook is the Speech-to-Text which handles the conversion of audio files to text. This is followed by the Sentiment Analysis section which extracts sentiments for each text sentence and displays those results in a format similar to the proposed dashboard.
|This notebook must be run before the model training and fine-tuning because the MP3 dataset must be downloaded and converted into the correct format.|
Support Center - Model Training and Fine-Tuning.ipynb
The TAO Toolkit virtual environment must be set up before executing the notebook (see the TAO Toolkit section in the Commands Overview for installation instructions).
This notebook relies on the TAO Toolkit to fine-tune deep learning models on the customers data. As with the previous notebook, this one is separated into two sections for the Speech-to-Text and Sentiment Analysis components. Each section goes through data processing, model training and fine-tuning, evaluation of results, and model export. Finally, there is an end section for deploying both your fine-tuned models for use in RIVA.