Everything You Need to Know About AutoML and Neural Architecture Search

AutoML and Neural Architecture Search (NAS) are the new kings of deep learning, offering a fast way to achieve high accuracy on machine‑learning tasks without extensive manual effort.

How NAS works : A set of possible “building blocks” is defined (e.g., the blocks proposed in the NASNet paper for image‑recognition networks). A controller recurrent neural network samples these blocks, assembles them into a complete architecture, and trains the resulting network on a validation set. The validation accuracy is used to update the controller via policy‑gradient, gradually improving the sampled architectures.

The NAS algorithm is intuitive: it repeatedly grabs different blocks, combines them into a network, trains and tests that network, and then adjusts the block selection and combination strategy based on the results.

Because NAS trains and evaluates architectures on a much smaller dataset than full‑scale ImageNet, it can discover models that perform well on larger datasets while keeping the search time manageable.

Research progress :

Progressive NAS (PNAS) replaces reinforcement learning with a sequential model‑based optimization (SMBO) strategy, exploring architectures from simple to complex and achieving 5‑8× higher efficiency than the original NAS.

Efficient NAS (ENAS) shares weights among all sampled models, turning the search into a form of transfer learning. This dramatically reduces training time; the authors report half‑day training on a single 1080 Ti GPU.

AutoML : Google Cloud AutoML provides a turnkey NAS service—users upload data, and Google’s algorithm returns a ready‑to‑use architecture. However, the service is expensive (≈$20 per use) and does not allow model export; users must call the provided API. Open‑source alternatives such as AutoKeras (built on ENAS) can be installed via pip and allow deeper inspection and modification.

Future outlook : While current NAS methods have become more efficient—finding a viable architecture in a day on a single GPU—search spaces remain limited to hand‑crafted blocks. The next breakthrough may involve truly open‑ended searches that discover novel building blocks, requiring even more efficient algorithms.

These advances present exciting challenges for the AI community and promise further breakthroughs in scientific research.