AutoML solutions overview

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/

 

Previous
Previous

Don’t be data-driven in AI

Next
Next

6 things you should know before beginning with AI projects