Warning: This post contains pictures of spiders (and Spiderman)!Google’s new Cloud AutoML Vision is a new machine learning service from Google Cloud that aims to make state of the art machine learning techniques accessible to non-machine learning experts. In this post I will show you how I was able, in just a few hours, to create a custom image classifier that is able to distinguish between different types of poisonous Australian spiders. I didn’t have any data when I started and it only required a very basic understanding of machine learning related concepts. I could probably show my Mum how to do it!
Recommendation systems are found under the hood of many popular services and websites. The e-commerce and retail industry use them to increase their sales, the music services provide interesting songs to their listeners, and the news sites rank the daily articles based on their readers interests. If you really think about it, recommendation systems can be used in pretty much every area of daily life. For example, why not automatically recommend better choices to house investors, guide your friends in your hometown without you being around, or suggest which company to apply to if you are looking for a job.
All pretty cool stuff, right!
But, recommendation systems need to be a lot smarter than a plain old vanilla software. In fact, the engine is made up of multiple machine learning modules that aim to rank the items of the interests for the users based on the users preferences and items properties.
In this blog series, you will gain some insight on how recommendation systems work, how you can harness Google Cloud Platform for scalable systems, and the architecture we used when implementing our music recommendation engine on the cloud. This first post will be a light introduction to the overall system, and my follow up articles will subsequently deep dive into each of the machine learning modules, and the tech that powers them.