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Google Unleashes the Power of AI and ML - Cloud AutoML to Help Businesses Build their Own AI Models

Google released a new AI (Artificial Intelligence) tool named Cloud AutoML, a product that empowers enterprises having limited ML (Machine Learning) expertise to harness Artificial Intelligence and develop high quality, custom AI models to improve their own products and services.

Google Unleashes the Power of AI and ML - Cloud AutoML to Help Businesses Build their Own AI Models

Google released a new AI (Artificial Intelligence) tool named Cloud AutoML, a product that empowers enterprises having limited ML (Machine Learning) expertise to harness Artificial Intelligence and develop high quality, custom AI models to improve their own products and services. You can train the machine learning systems on a photo dataset of your choice. Cloud AutoML will allow enterprises and developers to train custom vision models for their own use cases. "At Google Cloud, our goal has been to lower the barrier of entry and make AI available to the largest possible community of developers, researchers, and businesses. Our Google Cloud AI team has been making good progress towards this goal," Jia Li, Head of R&D, Cloud AI and ML, mentioned in a statement.

First to Release is Cloud AutoML Vision

Google's first Cloud AutoML release is Cloud AutoML Vision, a service that makes it faster and easier to create custom ML models for image recognition. Its drag-and-drop interface lets you easily upload images, train and manage models, and then deploy those trained models directly on Google Cloud. Early results using Cloud AutoML Vision to classify popular public datasets like ImageNet and CIFAR have shown more accurate results with fewer misclassifications than generic ML APIs. AutoML Vision provides a simple graphical user interface that lets businesses specify data, then turns that data into a high-quality model customizable on need basis.


Cloud AutoML Vision Capabilities
1) Increased accuracy: Cloud AutoML Vision is built on Google’s leading image recognition approaches, including transfer learning and neural architecture search technologies. This means you’ll get a more accurate model even if your business has limited machine learning expertise.
2) Faster turnaround time to production-ready models: With Cloud AutoML, you can create a simple model in minutes to pilot your AI-enabled application, or build out a full, production-ready model in as little as a day.
3) Easy to use: AutoML Vision provides a simple graphical user interface that lets you specify data, then turns that data into a high quality model customized for your specific needs.

How does Cloud AutoML Vision Work?

Training the AI does appear to be surprisingly simple. First, you’ll need a ton of tagged images. The minimum is 20, but the software supports up to 10,000. Using a meteorologist as an example for their promotional video was a smart choice by Google, not many people have thousands of tagged HD images bundled together and ready to upload.
A lot of image recognition is about identifying patterns. Once Google’s AI thinks it has a good understanding of what links together the images you’ve uploaded, it can be used to look for that pattern in new uploads, generating a number for how well it thinks the new images match it. So our meteorologist would eventually be able to upload images as the weather changes, identifying clouds while continuing to train and improve the software.


Impacting Millions of Lives, a Real Game Changer

The capability to recognize patterns at enormous scales has immense interdisciplinary value. Oncologists have trained machine learning systems on images of breast cancer cells so they can spot the disease earlier. Neuroscientists have used algorithms on MRI scans to predict language development in children. And Stanford researchers have applied similar software to predict race and voting patterns in cities by matching census data to the frequency of specific brands of cars. AutoML Vision is sure to initiate more projects like these because while early detection is potentially life-saving, this AI could also unravel new, as of yet unproven patterns and correlations. With AutoML Vision, the hurdle to entry is primarily data collection, i.e., capturing and correctly tagging thousands of images for training. There are more ways to capture images than ever, for example, via drones, cell phones, live feeds, or social media, but the means of capturing data is far from democratized


"AutoML Vision is the result of our close collaboration with the Google Brain and other Google AI teams, and is the first of several Cloud AutoML products in development that aims to make it easier for more businesses to adopt ML," Li said. While AutoML still at the nascent stage of its journey to make AI more accessible, Google has been deeply inspired by what its 10,000+ customers using Cloud AI products have been able to achieve. It’s certain that the release of Cloud AutoML will help even more businesses discover what’s possible through AI.



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