Introduction I have been looking for a list of AutoML solutions and a way to compare them, but I haven’t been able to find it. So I thought I might as well compile that list for others to use. If you are not familiar with AutoML read this post for a quick introduction and pros and cons. I haven’t been able to test them all and make a proper review, so this is just a comparison based on features. I tried to pick the features that felt most important to me, but it might not be the most important for you. If you think some features are missing or if you know an AutoML solution that should be on the list, just let me know. Before we go to the list I’d just quickly go through the features and how I interpret them. FeaturesDeployment Some solutions can be auto deployed directly to the cloud with a 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.AccessibleSome 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 toolSome have an internal labelling tool so you can directly label data before training the model. That can be very useful in some cases.General / SpecializedMost 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 SourceSelf-explanatory. Is it open source or not.Includes transfer LearningTransfer 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. AutoML solutions list Google AutoML Google AutoML is the one I’m the most familiar with. I found it pretty easy to use even without coding. The biggest issue I’ve had is that the API requires a bunch of setup and is not just a simple token or Oauth-based authentication. Deployment: To cloud, export, edgeTypes: Text, Images, Video, TabularExplainable: LittleMonitor: NoAccessible: VeryLabeling tool: Used to have but is closedGeneral / Specialized: GeneralizedOpen Source: NoIncludes transfer Learning: YesLink: https://cloud.google.com/automl Azure AutoMLMicrosoft’s cloud AutoML seems to be more Xplainable than Google’s but with only tabular data models. Deployment: To cloud, some LocalTypes: Only TabularExplainable: SomeMonitor: NoAccessible: VeryLabeling tool: NoGeneral / Specialized: GeneralizedOpen Source: NoIncludes transfer Learning: YesLink: https://azure.microsoft.com/en-us/services/machine-learning/automatedml/Lobe.AIThis solution is still in beta but works very well in my experience. I’ll write a review as soon as it goes public. Lobe is so easy to use that you can let a 10-year old use it to train deep learning models. I’d really recommend this for education purposes. Deployment: Local and export to TensorflowTypes: ImagesExplainable: LittleMonitor: -Accessible: Very – A third grader can use thisLabeling tool: YesGeneral / Specialized: GeneralizedOpen Source: NoIncludes transfer Learning: YesLink: https://lobe.ai/ KorticalKortical seems to be one the AutoML solutions that differentiates itself by being as explainable as possible. This can be a huge advantage when not just trying to get good results but also understand the business problem better. For that I’m a bit of a fan.Deployment: To cloudTypes: TabularExplainable: VeryMonitor: NoAccessible: VeryLabeling tool: NoGeneral / Specialized: GeneralizedOpen Source: NoIncludes transfer Learning: Not sureLink: https://kortical.com/DataRobotA big player that might even be the first pure AutoML to go IPO.Deployment: To cloudTypes: Text, Images and TabularExplainable: VeryMonitor: YesAccessible: VeryLabeling tool: NoGeneral / Specialized: GeneralizedOpen Source: NoIncludes transfer Learning: YesLink: https://www.datarobot.com/platform/automated-machine-learning/ AWS Sagemaker AutopilotAmazons AutoML. Requires more technical skills than the other big cloud suppliers and is quite limited and supports only two algorithms: XGBoost and Logistic regression. Deployment: To cloud and exportTypes: TabularExplainable: SomeMonitor: YesAccessible: Requires codingLabeling tool: YesGeneral / Specialized: GeneralizedOpen Source: NoIncludes transfer Learning: YesLink: https://aws.amazon.com/sagemaker/autopilot/MLJar Deployment: Export and CloudTypes: TabularExplainable: YesMonitor: -Accessible: VeryLabeling tool: NoGeneral / Specialized: GeneralizedOpen Source: MLJar has both and Open source(https://github.com/mljar/mljar-supervised ) and closed source solution.Includes transfer Learning: YesLink: https://mljar.com/Autogluon Deployment: ExportTypes: Text, Images, tabularExplainable: -Monitor: -Accessible: Requires codingLabeling tool: NoGeneral / Specialized: GeneralizedOpen Source: YesIncludes transfer Learning: YesLink: https://autogluon.mxnet.io/JadBio Deployment: Cloud and ExportTypes: TabularExplainable: SomeMonitor: NoAccessible: VeryLabeling tool: NoGeneral / Specialized: LifeScienceOpen Source: NoIncludes transfer Learning: -Link: https://www.jadbio.com/ AUTOWEKAThis solution supports Bayesian models which is pretty cool. Deployment : ExportTypes: -Explainable: -Monitor: -Accessible: Requires CodeLabeling tool: NoGeneral / Specialized: GeneralizedOpen Source: YesIncludes transfer Learning:NoLink: https://www.cs.ubc.ca/labs/beta/Projects/autoweka/ H2o Driverless AI Also supports bayesian modelsDeployment: ExportTypes: -Explainable: -Monitor: -Accessible: SemiLabeling tool: NoGeneral / Specialized: GeneralizedOpen Source: Both optionsIncludes transfer Learning: -Link: https://www.h2o.ai/ AutokerasAutokeras is one of the most popular open source solutions and is definitely worth trying out.Deployment: ExportTypes: Text, Images, tabularExplainable: PossibleMonitor: -Accessible: Requires CodeLabeling tool: NoGeneral / Specialized: GeneralizedOpen Source: YesIncludes transfer Learning: -Link: https://autokeras.com/ TPOT Deployment: ExportTypes: Images and TabularExplainable: PossibleMonitor: -Accessible: Requires CodeLabeling tool: NoGeneral / Specialized: GeneralizedOpen Source: YesIncludes transfer Learning: -Link: http://epistasislab.github.io/tpot/ PycaretDeployment: ExportTypes: Text, TabularExplainable: PossibleMonitor: -Accessible: Requires CodeLabeling tool: NoGeneral / Specialized: GeneralizedOpen Source: YesIncludes transfer Learning: -Link: https://github.com/pycaret/pycaretAutoSklearnDeployment: ExportTypes: TabularExplainable: PossibleMonitor: -Accessible: Requires CodeLabeling tool: NoGeneral / Specialized: GeneralizedOpen Source: YesIncludes transfer Learning: -Link: https://automl.github.io/auto-sklearn/master/TransmogrifAIMade by Salesforce.Deployment: ExportTypes: Text and TabularExplainable: PossibleMonitor: -Accessible: Requires CodeLabeling tool: NoGeneral / Specialized: GeneralizedOpen Source: YesIncludes transfer Learning: -Link: https://transmogrif.ai/
