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.
Features
Deployment
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.
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.
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, edge
Types: Text, Images, Video, Tabular
Explainable: Little
Monitor: No
Accessible: Very
Labeling tool: Used to have but is closed
General / Specialized: Generalized
Open Source: No
Includes transfer Learning: Yes
Link: https://cloud.google.com/automl
Azure AutoML
Microsoft’s cloud AutoML seems to be more Xplainable than Google’s but with only tabular data models.
Deployment: To cloud, some Local
Types: Only Tabular
Explainable: Some
Monitor: No
Accessible: Very
Labeling tool: No
General / Specialized: Generalized
Open Source: No
Includes transfer Learning: Yes
Link: https://azure.microsoft.com/en-us/services/machine-learning/automatedml/
Lobe.AI
This 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 Tensorflow
Types: Images
Explainable: Little
Monitor: –
Accessible: Very – A third grader can use this
Labeling tool: Yes
General / Specialized: Generalized
Open Source: No
Includes transfer Learning: Yes
Link: https://lobe.ai/
Kortical
Kortical 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 cloud
Types: Tabular
Explainable: Very
Monitor: No
Accessible: Very
Labeling tool: No
General / Specialized: Generalized
Open Source: No
Includes transfer Learning: Not sure
Link: https://kortical.com/
DataRobot
A big player that might even be the first pure AutoML to go IPO.
Deployment: To cloud
Types: Text, Images and Tabular
Explainable: Very
Monitor: Yes
Accessible: Very
Labeling tool: No
General / Specialized: Generalized
Open Source: No
Includes transfer Learning: Yes
Link: https://www.datarobot.com/platform/automated-machine-learning/
AWS Sagemaker Autopilot
Amazons 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 export
Types: Tabular
Explainable: Some
Monitor: Yes
Accessible: Requires coding
Labeling tool: Yes
General / Specialized: Generalized
Open Source: No
Includes transfer Learning: Yes
Link: https://aws.amazon.com/sagemaker/autopilot/
MLJar
Deployment: Export and Cloud
Types: Tabular
Explainable: Yes
Monitor: –
Accessible: Very
Labeling tool: No
General / Specialized: Generalized
Open Source: MLJar has both and Open source(https://github.com/mljar/mljar-supervised ) and closed source solution.
Includes transfer Learning: Yes
Link: https://mljar.com/
Autogluon
Deployment: Export
Types: Text, Images, tabular
Explainable: –
Monitor: –
Accessible: Requires coding
Labeling tool: No
General / Specialized: Generalized
Open Source: Yes
Includes transfer Learning: Yes
Link: https://autogluon.mxnet.io/
JadBio
Deployment: Cloud and Export
Types: Tabular
Explainable: Some
Monitor: No
Accessible: Very
Labeling tool: No
General / Specialized: LifeScience
Open Source: No
Includes transfer Learning: –
Link: https://www.jadbio.com/
AUTOWEKA
This solution supports Bayesian models which is pretty cool.
Deployment : Export
Types: –
Explainable: –
Monitor: –
Accessible: Requires Code
Labeling tool: No
General / Specialized: Generalized
Open Source: Yes
Includes transfer Learning:No
Link: https://www.cs.ubc.ca/labs/beta/Projects/autoweka/
H2o Driverless AI
Also supports bayesian models
Deployment: Export
Types: –
Explainable: –
Monitor: –
Accessible: Semi
Labeling tool: No
General / Specialized: Generalized
Open Source: Both options
Includes transfer Learning: –
Link: https://www.h2o.ai/
Autokeras
Autokeras is one of the most popular open source solutions and is definitely worth trying out.
Deployment: Export
Types: Text, Images, tabular
Explainable: Possible
Monitor: –
Accessible: Requires Code
Labeling tool: No
General / Specialized: Generalized
Open Source: Yes
Includes transfer Learning: –
Link: https://autokeras.com/
TPOT
Deployment: Export
Types: Images and Tabular
Explainable: Possible
Monitor: –
Accessible: Requires Code
Labeling tool: No
General / Specialized: Generalized
Open Source: Yes
Includes transfer Learning: –
Link: http://epistasislab.github.io/tpot/
Pycaret
Deployment: Export
Types: Text, Tabular
Explainable: Possible
Monitor: –
Accessible: Requires Code
Labeling tool: No
General / Specialized: Generalized
Open Source: Yes
Includes transfer Learning: –
Link: https://github.com/pycaret/pycaret
AutoSklearn
Deployment: Export
Types: Tabular
Explainable: Possible
Monitor: –
Accessible: Requires Code
Labeling tool: No
General / Specialized: Generalized
Open Source: Yes
Includes transfer Learning: –
Link: https://automl.github.io/auto-sklearn/master/
TransmogrifAI
Made by Salesforce.
Deployment: Export
Types: Text and Tabular
Explainable: Possible
Monitor: –
Accessible: Requires Code
Labeling tool: No
General / Specialized: Generalized
Open Source: Yes
Includes transfer Learning: –
Link: https://transmogrif.ai/
