Intro to ML: Image Processing
Introductory 5 Steps 5 hours 25 Credits
Using large scale computing power to recognize patterns and "read" images is one of the foundational technologies in AI, from self-driving cars to facial recognition. The Google Cloud Platform provides world class speed and accuracy via systems that can utilized by simply calling APIs. With these and a host of other APIs, GCP has a tool for just about any machine learning job. In this introductory quest, you will get hands-on practice with machine learning as it applies to image processing by taking labs that will enable you to label images, detect faces and landmarks, as well as extract, analyze, and translate text from within images.
In this lab you train and deploy a TensorFlow model to Cloud ML Engine for serving (prediction). Watch these short videos Harness the Power of Machine Learning with Cloud ML Engine and Cloud ML Engine: Qwik Start - Qwiklabs Preview.
In this lab you'll upload an image to Cloud Storage then make a request to the Vision API with APIs Explorer.
AutoML Vision helps developers with limited ML expertise train high quality image recognition models. In this hands-on lab, you will learn how to train a custom model to recognize different types of clouds (cumulus, cumulonimbus, etc.).
The Cloud Vision API lets you understand the content of an image by encapsulating powerful machine learning models in a simple REST API. In this lab you’ll send an image to the Cloud Vision API and have it identify objects, faces, and landmarks.
In this lab you’ll combine the Cloud Vision, Natural Language, and Translation APIs to capture text strings from images, recognize characters, and analyze and translate the text strings into other languages.