Google has launched a drag and drop development system for cloud-based machine learning (ML) to simplify the implementation of artificial intelligence (AI) systems. As we have said consistently, linking embedded system to these capabilities both at the edge and in the cloud is driving the roll out of effective networks in the Internet of Things (IoT).
The problem is that currently, only a handful of businesses in the world have access to the talent and budgets needed to fully appreciate the advancements of ML and AI. There’s a very limited number of people that can create advanced machine learning models, and the process is still time-intensive and complicated to build a custom ML model. While Google has offered pre-trained machine learning models via APIs that perform specific tasks, there's still a long road ahead.
Cloud AutoML uses techniques like learning2learn and transfer learning to help huild ML models for machine vision, helping AI experts be even more productive and less-skilled engineers build powerful AI systems.
The first Cloud AutoML release will be Cloud AutoML Vision, a service that makes it faster and easier to create custom ML models for image recognition. The drag-and-drop interface lets developers 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.
Cloud AutoML Vision is built on Google’s leading image recognition approaches, including transfer learning and neural architecture search technologies for a more accurate model in minutes to pilot an AI-enabled application, or for a full, production-ready model in as little as a day. It provides a simple graphical user interface that lets designers specify data, then turns that data into a customised model.
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