Notes on Enabling AI in Applications with Google Cloud

Introduction

• No prior experience in machine learning is needed to integrate AI into applications.
• Google Cloud simplifies the process; you can enable AI in seconds.

Importance of Pre-trained Models

• Building AI models from scratch can be time-consuming and requires a lot of data.
• Google offers pre-trained APIs, providing instant access to advanced AI tools.
• This approach eliminates the need to gather data, learn AI technology, train models, and update them.

AI Toolbox Overview

• Google has a vast library of machine learning APIs:
  • Computer Vision
  • Speech Models (supports 70+ languages and 137 variants)
  • Language Translation
  • Speech Transcription
  • Sentiment Analysis
  • Speech Synthesis
• Continuous updates are provided to keep the models current with innovations from DeepMind and Google Research.

Demonstration: Enabling Speech-to-Text API

1. Access Google Cloud Platform Dashboard

   • This serves as the project's home page.

2. Enable Speech-to-Text API

   • Navigate through the hamburger menu to find Speech to Text.
   • Click on "Enable the API."

3. Create a Storage Bucket

   • If no bucket exists, create one (e.g., MyFirstBucketTTS).
   • This bucket stores files uploaded to Google Cloud.

4. Local Upload

   • Select a sample audio file from your device.
   • Metadata for the file is automatically read in.
   • Specify the language and dialect of the audio (e.g., South African English).
   • Submit the file for processing.

5. View Results

   • Results will be displayed upon submission.

Future Videos

• Upcoming content will cover:
  • Automating transcription for multiple files using code.
  • Blending different APIs together for enhanced functionality.

Conclusion

• Encouragement to try Google Cloud services for free today.
• Access to innovative AI tools is easy and efficient.