Learn how to design machine learning solutions with Google Cloud Platform. Review services such as AutoML, CloudML Engine, and the GCP machine learning APIs.
Machine learning can make your applications faster and more intelligent. You can analyze customer data such as voice and text input, images, and video, and take action without human intervention. Google Cloud Platform (GCP) offers a competitive set of machine learning services for nearly every type of architecture, including serverless computing, containers, and virtual machines. Learn how to design your own machine learning solutions using GCP, in this introductory course with instructor Lynn Langit. Lynn shows how to identify your requirements and map them to services such as the GCP machine learning APIsâCloud Vision, Cloud Speech-to-Text, Cloud Video Intelligence, and moreâand GCP AutoML, which puts the same APIs behind an easy-to-use interface. Then get an overview of the custom ML models and deep neural networks that are possible in Google Cloud ML Engine. Finally, review five different practical examples of GCP machine learning, including a chat bot, an image search application, and an on-device Internet of Things application.
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