I didn't downvote you, but I think the comparison to Sagemaker misses the point. This is literally just uploading labeled data and getting a finely tuned classi

I didn't downvote you, but I think the comparison to Sagemaker misses the point. This is literally just uploading labeled data and getting a finely tuned classifier out. Hyperparameter tuning is neat, and both Cloud ML Engine and Sagemaker have that, but (correct me if I'm wrong), only AutoML actually handles all of the model architecture decisions itself using transfer learning and learning2learn. See here for details: https://research.googleblog.com/2017/11/automl-for-large-scale-image.html https://research.googleblog.com/2017/11/automl-for-large-sca... This significantly reduces the level of expertise required to train models, and the AutoML models outperform "expert" human-created architectures.