NAS on Vertex AI overview
Vertex AI NAS is an optimization technique that can be leveraged to find the best neural network architecture for a given ML use case. NAS-based optimization searches for the best network in terms of accuracy but can also be augmented with other constraints such as latency, memory, or a custom metric as per the requirements. In general, the search space of possible neural networks can be quite large and NAS may support a search space as large as 10^20. In the past few years, NAS has been able to successfully generate some state-of-the-art computer vision network architectures, including NASNet, MNasNet, EfficientNet, SpineNet, NAS-FPN, and so on.
It may seem complex, but NAS features are quite flexible and easy to use. A beginner can leverage prebuilt modules for search spaces, trainer scripts, and Jupyter notebooks to start exploring Vertex AI NAS on a custom dataset. If you are an expert, you could potentially develop custom trainer scripts, custom search...
