How Google’s AutoML Takes Machine Learning A Step Further

Last week Google announced an alpha launch of AutoML, a new machine learning platform that takes the space to the next level. In the product announcement, AutoML is described as a service that allows users to train machine learning models without requiring an in-depth knowledge and proficiency in machine learning. The potential of this platform is very appealing:

  • It promises to eliminate the mental and financial barriers smaller companies face when attempting to integrate state-of-the-art machine learning into their platform.
  • AutoML will move machine learning from behind its theoretical walled garden, still mostly accessible only to those with academic backgrounds or the capital to hire top candidates, and into the space of the real and practical.
  • It will help business align human intellectual capital to innovate and differentiate.

On a more technical level, AutoML is just as impressive:

  • AutoML utilizes transfer learning to train its users’ models without loading large amounts of data. Transfer learning allows models to obtain knowledge from other models as opposed to gaining it from raw data. This will cut down on time required to make informed business decisions.
  • AutoML provides a platform for automating object detection and image tagging. These features are not new to the Google platform or its competitors — Clarifai, Hive AI and  Microsoft Cognitive Services also offer these services. However, AutoML differentiates itself from the rest by empowering users to provide the platform a sample set of images with specific tags called out. The system learns about these images and the tags therein. Once the model is trained, an unseen sample will accurately detect and tag the items based on the learned model.

Related: Understanding the State of Machine Learning Services

The Filestack Take

Filestack loves AutoML’s focus on simplicity and efficiency. However, we believe the industry needs automated, easy solutions for the more difficult use cases. For example, what if the use case requires object detection, object localization and optical character recognition (OCR) to work together? Lucky for you, Filestack does just this.

Filestack provides a platform where custom models utilize multiple ML services. All the integration needs is a couple of lines of code within our customer’s software stack. We provide our customers with the most compelling feature set across all the leading machine learning platforms. Customers integrate Filestack’s API into their software stack to ensure that edge content gets pushed to the cloud reliably, securely and intelligently. It is processed, transformed and analyzed to ensure that it is in the exact state that the application expects it.

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We recently announced customized trained machine learning models for deriving relevant information from physical mail envelope scans as well as from scanned bank checks. This is what Steven Maguire, Vp of Technology at Earth Class Mail had to say about his experience with this service:

“A simple integration with Filestack’s content intelligence API allowed us to offload the analysis of a large volume of mail quickly and reliably. This automation has proven critically important as our team has more time to focus on enhancing our core product.”

Steven Maguire, VP Technology at Earth Class Mail

We are looking into the capabilities of AutoML and are investigating ways to integrate with it. In the mean time, we’d love to hear your thoughts on how machine learning may help transform your business.

How do you think ML will change the game for you?

FAQs about Google AutoML

Is Google AutoML free?

Google AutoML has a free trial. However, it doesn’t have a free plan, meaning you have to pay after the trial expires. The amount you have to pay depends on your usage and resources required.

How good is Google AutoML?

The AutoML Google service is a great one because it lets organizations integrate machine learning functionalities in their applications easily. It also removes any technical worries from businesses. For more information, check out this page, which explains what is autoML Google. The same goes for Filestack’s intelligence features, such as object detection, OCR, virus detection, explicit content detection, and more. In the end, services that offer easy (or easier than usual) implementations of ML or AI always have benefits.

How do I use Google AutoML?

To start using Google AutoML, follow the following steps:

  1. Sign up for a Google Cloud account
  2. Create a new project and enable the AutoML API
  3. Upload some relevant, preprocessed data
  4. Create a new AutoML model, then select the problem type and training data you want
  5. Evaluate the performance of the model and make necessary adjustments
  6. Deploy the model to your applications


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