Google Cloud Platform for Machine Learning Essential Training

Introduction

• Build complete solutions with machine learning services
• What you should know
• About using cloud services

1. Machine Learning on Google Cloud Platform

• Business scenarios for machine learning
• Which algorithm should you use?
• GCP AI servers vs. platforms
• Enable GCP ML APIs
• Data preparation with Cloud Dataflow and Cloud Dataprep
• An ML notebook in action: Colaboratory
• An ML notebook in action: Set up Cloud Datalab
• An ML notebook in action: Use Cloud Datalab

2. Machine Learning API Services

• Overview of GCP ML APIs
• Predict via the Cloud Vision API for images
• Predict via the Cloud Video Intelligence API for video
• Predict via the Natural Language API for NLP
• Predict via the Text-to-Speech API
• Predict via the Speech-to-Text API
• Predict via the Cloud Translation API
• Predict via BigQuery ML

3. Machine Learning with AutoML

• Understand Cloud AutoML services
• Understand AutoML Vision
• Prepare data and labels for AutoML Vision
• Train model for AutoML Vision
• Evaluate model with AutoML Vision
• Predict using a trained AutoML Vision model

4. Advanced Machine Learning

• Why build custom ML models?
• Using containers to host ML models
• Use Cloud ML Engine
• Evaluate Cloud ML Engine output
• Scale custom ML models
• Understanding deep learning
• Work with TensorBoard
• Work with Keras for TensorFlow
• GPUs and TPUs for TensorFlow
• TensorFlow for JavaScript and mobile

5. Machine Learning Architectures

• Chatbot with ML
• Image search with Cloud Vision and Cloud ML
• GCP serverless machine learning architecture
• GCP machine learning with structured data
• GCP ML service for IoT apps

Conclusion

• Next steps