Cloud SQL can serve all the AI and ML use cases for large and complex structured data. Another service named Cloud Spanner can serve similar use cases, which can be served by Cloud SQL, but on a very large scale.
Cloud SQL and AI applications
by Anand Deshpande,
Manish Kumar,
Vikram Chaudhari
Hands-On Artificial Intelligence on Google Cloud Platform
- Title Page
- About Packt
- Copyright and Credits
- Contributors
- Preface
- Section 1: Basics of Google Cloud Platform
- Overview of AI and GCP
- Computing and Processing Using GCP Components
- Section 2: Artificial Intelligence with Google Cloud Platform
- Machine Learning Applications with XGBoost
- Using Cloud AutoML
- Overview of Cloud AutoML 
- Document classification using AutoML Natural Language
- Image classification using AutoML Vision APIs
- Performing speech-to-text conversion using the Speech-to-Text API
- Sentiment analysis using AutoML Natural Language APIs
- Summary
- Building a Big Data Cloud Machine Learning Engine
- Understanding ML
- Understanding how to use Cloud Machine Learning Engine
- Overview of the Keras framework 
- Training your model using the Keras framework
- Training your model using Google AI Platform
- Asynchronous batch prediction using Cloud Machine Learning Engine
- Real-time prediction using Cloud Machine Learning Engine
- Summary
- Smart Conversational Applications Using DialogFlow
- Section 3: TensorFlow on Google Cloud Platform
- Understanding Cloud TPUs
- Implementing TensorFlow Models Using Cloud ML Engine
- Building Prediction Applications
- Section 4: Building Applications and Upcoming Features
- Building an AI application
- A step-by-step approach to developing AI applications
- Overview of the use case – automated invoice processing (AIP)
- Designing AIP with AI platform tools on GCP
- Upcoming features
- Summary
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Cloud SQL and AI applications
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