https://medium.com/ai%C2%B3-theory-practice-business/end-to-end-automl-for-tabular-data-at-kaggle-days-c2e4b714dcac

Google’s AutoML efforts aim to make ML more scalable and accelerate both research and industry applications. Our initial efforts of neural architecture search have enabled breakthroughs in computer vision with NasNet, and evolutionary methods such as AmoebaNet and hardware-aware mobile vision architecture MNasNet further show the benefit of these learning-to-learn methods. Recently, we applied a learning-based approach to tabular data, creating a scalable end-to-end AutoML solution that meets three key criteria:

• Full automation: Data and computation resources are the only inputs, while a servable TensorFlow model is the output. The whole process requires no human intervention.

• Extensive coverage: The solution is applicable to the majority of arbitrary tasks in the tabular data domain.

• High quality: Models generated by AutoML has comparable quality to models manually crafted by top ML experts.