Dan Rose, an entrepreneur, and AI enthusiast, writes for Dan Rose AI blog about how our brains are ‘melting’ into computers, or how AI looks twenty years from now. In his recent blog post “AutoML solutions overview” – a wanna-be qualitative comparison – he has been looking for a list of AutoML solutions and a way to compare them. Not much information out there but he decided to compile a list, with his limited findings, for anyone who might believe its useful ?.

If you are not familiar with AutoML, read this post for a quick introduction.

The list includes key players in the field like Google AutoML, Azure AutoML, Lobe.AI, Kortical, DataRobot, AWS Sagemaker Autopilot, MLJar, Autogluon, JADBio (us), AUTOWEKA, H2o Driverless AI, Autokeras, etc. Dan Rose hasn’t been able to test them all, so this is just a comparison based on features. He tried to pick the features that felt most important to him. Anyway, he is open to suggestions if you think some features are missing or if you know an AutoML solution that should be on the list.

It’s interesting to list his criteria and which features he considers essential:

Criteria/Features

Deployment 
Some solutions can be auto deployed directly to the cloud with one-click deployment. Some just export to Tensorflow and some even have specific export to edge devices.

Types 
This can be Text, Images, video, tabular. I guess some of the open-source ones can be stretched to do anything if put in the work, so it might not be the complete truth.

Explainable 
Explainability in AI is a hot topic and a very important feature for some projects. Some solutions give you no insights and some gives you a lot and it might even be a strategic differentiator for the provider. I have simply divided this feature into Little, Some and Very Explainable.

Monitor 
Monitoring models after deployment to avoid drifting of models can be a very useful feature. I divided this into Yes and No.

Accessible
Some of the providers are very easy to use and some of them require coding and at least basic data science understanding. So I took this feature in so you can pick the tool that corresponds to the abilities you have access to.

Labeling tool
Some have an internal labelling tool so you can directly label data before training the model. That can be very useful in some cases.

General / Specialized
Most AutoML solutions are generalized for all industries but a few are specialized to specific industries. I suspect this will become more popular, so I took this feature in.

Open Source
Self-explanatory. Is it open source or not.

Includes transfer Learning
Transfer learning is one of the big advantages of AutoML. You get to piggyback on big models so you can get great results with very little data.

Read the full AutoML solutions overview on the Dan Rose AI blog for an objective overview.

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