Machine Learning APIs
Advanced 9 Steps 8 hours 53 Credits
It’s no secret that machine learning is one of the fastest growing fields in tech, and the Google Cloud Platform has been instrumental in furthering it’s development. With a host of APIs, GCP has a tool for just about any machine learning job. In this advanced-level quest, you will get hands-on practice with machine learning APIs by taking labs like Implementing an AI Chatbot with Dialogflow and Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API.
Prerequisites:This Quest requires hands-on experience with GCP’s machine learning services. Make sure that you have at least earned a Badge by completing the hands-on labs in the Baseline: Data, ML, and AI Quest before beginning—the labs in this series assume foundational ML knowledge and will explore advanced features through specific use cases.
This lab will teach you about the architecture and basic functioning of Application Programming Interfaces (APIs). This will be supplemented with hands-on practice, where you will configure and run Cloud Storage API methods in Cloud Shell.
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.
In this lab you’ll learn how to classify text into categories using the Natural Language API
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.
The Cloud Natural Language API lets you extract entities, and perform sentiment and syntactic analysis on a block of text. In this hands-on lab you’ll learn how to extract entities and sentiment from text using the Cloud Natural Language API.
This hands-on lab uses Kubernetes and Cloud Vision API to create an example of how to use the Vision API to classify (label) images from Reddit’s /r/aww subreddit and display the labelled results in a web app.
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 goal of this lab is to introduce the basics of Google Cloud Dialogflow by building a responsive chat bot, such as those handling support requests on websites. Demonstrates how to utilize this interactive AI in application development.
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.