Machine learning (ML) is transforming the way every industry operates and when Google is one of the key drivers of this technology, you can guarantee this includes paid advertising. In fact, marketers who rely on processing data in order to reach their target audiences stand to gain the most from machine learning as it continues to evolve.
The only problem is developing advanced machine learning algorithms isn’t just expensive, it also requires the best programming minds on the planet. Unless, of course, those same minds create platforms that make (ML) development easier for everyone.
Which is precisely what Google aims to do with its new Cloud AutoML platform.
AutoML makes machine learning accessible to everyone
The purpose of AutoML is to give developers with limited machine learning experience the tools they need to build and train machine learning algorithms. Let’s say you’re in a scenario where you know machine learning can improve your advertising workflow but you don’t know how to code the necessary algorithm itself.
You know what you want to do and where the data needs to come from but you don’t have the programmatic skills to make the machine learning magic happen. AutoML is there to provide this magic for you, giving you the freedom to implement machine learning in a way that normally requires a full team of programmers and data scientists.
Essentially, AutoML does for machine learning what WordPress does for web development. It opens the technology to anyone with the right idea, regardless of their technical skills.
Starting with image recognition
Google’s first AutoML release will be Cloud AutoML Vision, a platform that makes it easier to create machine learning models for image recognition. Using the drag-and-drop interface, you can upload your images, train your ML models and deploy them once they meet your accuracy targets.
In terms of advertising, AutoML Vision won’t be the most groundbreaking version of this technology. One use case that will come in handy is auto-generating product attributes based on images. Once your ML models can recognise brand logos, product types (eg: laptops vs TVs) and colours, you won’t have to assign these attributes manually. Users can search for products by specific attributes but you no longer have to assign them manually to every new product.
Another example of using AutoML Vision could be optimising images for search – a task that can be time consuming to do manually.
Time to get involved with machine learning
Google’s Cloud AutoML isn’t fully released yet but you can request access by visiting this page. We expect the platform will continue to expand and cater for other aspects of machine learning beyond image recognition over the coming years. You don’t need to wait for AutoML to get involved in machine learning, though. Google’s TensorFlow, Microsoft’s Azure and Amazon’s AWS are among the other platforms making machine learning more accessible and powerful to developers without the ML expertise it would normally require.
As machine learning becomes more accessible to everyone, now is the time to start thinking about how you can improve your advertising and marketing efforts with the technology.