There are many implementations of AutoML that you can try. Some are paid services, and some are free source code. The lists below are by no means complete or final.
All of the big three cloud services have some kind of AutoML. Amazon SageMaker does hyperparameter tuning but doesn’t automatically try multiple models or perform feature engineering. Azure Machine Learninghas both AutoML, which sweeps through features and algorithms, and hyperparameter tuning, which you typically run on the best algorithm chosen by AutoML. Google Cloud AutoML, as I discussed earlier, is deep transfer learning for language pair translation, natural language classification, and image classification.
A number of smaller companies offer AutoML services as well. For example, DataRobot, which claims to have invented AutoML, has a strong reputation in the market. And while dotData has a tiny market share and a mediocre UI, it has strong feature engineering capabilities and covers many enterprise use cases. H2O.ai Driverless AI, which I reviewed in 2017, can help a data scientist turn out models like a Kaggle master, doing feature engineering, algorithm sweeps, and hyperparameter optimization in a unified way.
AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. Auto-Kerasis an open source software library for automated machine learning, developed at Texas A&M, that provides functions to automatically search for architecture and hyperparameters of deep learning models. NNI(Neural Network Intelligence) is a toolkit from Microsoft to help users design and tune machine learning models (e.g., hyperparameters), neural network architectures, or a complex system’s parameters in an efficient and automatic way.