There are a variety of other AutoML systems for tabular data that are out there, each with their own strengths and weaknesses. The success of AutoML research has even sparked large companies such as Microsoft, Amazon and Google to develop their own bespoke AutoML systems.
One of the earlier AutoML Systems, TPOT uses evolutionary search methods to find pipelines from simple to exotic for to maximize performance.
Another customizable AutoML tool using multi-fidelity optimization and ensembling as efficient techniques to find pipelines.
Both an open-source and paid service, H20 was one of the first companies to offer AutoML as a service, using ensembling techniques to find performant and complimentary pipelines.
Microsoft – FLAML
FLAML boasts a dominant performance when your budget to find a suitable model is small … very small. When applications depend on fast AutoML for user applications such as spreadsheets, timing is everything. By efficiently trying out a large variety of cheap models, they obtain good results and fast. While other AutoML systems boast a higher performance given enough time, FLAML gives decent results and fast.
Amazon – AutoGluon
AutoGluon is Amazon’s answer to AutoML for practitioners. Even without HPO, AutoGluon achieves state of the art with an exhaustive approach to meta-learning what models are generally good for a given problem and using enemsemble techniques like stacking, they provide efficient models and fast. AutoGluon also has support for a variety of modalities beyond just simple tabular csv’s, also incorporating text and images into their inference pipelines.
Google – Google AutoML
The success of AutoML systems in enabling non machine learning to understand and utilize their data has lead to Google’s own cloud based product, Google AutoML. This enables evens practitioners with limited compute to utilize the cloud for their problems.