There’s lots of buzz about Machine Learning and what it can do: on land, at sea, in hospitals, on the chessboard, even extraterrestrial applications. How can you take advantage of ML for your project, here on planet earth? To get the most out of Google Cloud, you want to know how to use ML tools. Not just a high-level understanding of what ML is (though that’s helpful too) – you want to know how ML can help you meet your goals.
And there’s a lab for that! The new 7-lab Machine Learning APIs Quest walks you through use cases and scenarios with Google ML tools. Use these labs to help you understand what ML can do for you. Then, try using ML for your own projects.
Here are some of the labs in the new Quest:
Cloud ML Engine: Qwik start – Train a TensorFlow model to predict income category of a person using the United States Census Income Dataset, both locally and on Cloud ML Engine. To build your model, you’ll use TensorFlow’s prebuilt DNNCombinedLinearClassifier Estimator (this model is sometimes called ‘Wide & Deep’).
AI Chatbot: OK Google! I want to change my password – Build an application to submit tickets with a network and 3 subnetworks that you will use throughout the lab.
Awwvision: Cloud Vision API from a Kubernetes Cluster – With a 4+ star rating already, the Awwvision lab uses Kubernetes and Cloud Vision API to demonstrate how to use the Vision API to classify Reddit’s /r/aww subreddit images and display the labelled results in a web app.
Hundreds of companies are hiring Machine Learning engineering roles. Set yourself apart with a Google Cloud ML credential. Complete the Quest and add the Machine Learning APIs badge to your online profiles and your resume, to show your experience with in-demand ML skills.