AutoML software
Auto Machine learning (AutoML) software enables data scientists and machine learning engineers as well as non-technical users, to automatically build scalable machine learning models +Show More
Products | Position | Customer satisfaction | |||
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Leader
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Satisfactory
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DataRobot's automated machine learning platform makes it fast and easy to build and deploy accurate predictive models.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Number of case studies
40-50 case studies
Company's number of employees
1k-2k employees
Company's social media followers
100k-1m followers
Total funding
$1-5bn
# of funding rounds
11
Latest funding date
June 27, 2021
Last funding amount
$250-500m
Company
Type of company
private
Founding year
2012
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Leader
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Satisfactory
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Dataiku's single, collaborative platform powers both self-service analytics and the operationalization of machine learning models in production.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Number of case studies
50-100 case studies
Company's number of employees
1k-2k employees
Company's social media followers
100k-1m followers
Total funding
$1-1bn
# of funding rounds
11
Latest funding date
December 12, 2022
Last funding amount
$100-250m
Company
Type of company
private
Founding year
2013
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Leader
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Satisfactory
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H2O.ai is the leading AI Cloud company, on a mission to democratize AI and drive an open AI movement around the world. They focus on drawing insights from structured and unstructured data like video and documents with their award-winning products like Hydrogen Torch and Document AI. Customers use the H2O AI Cloud to rapidly solve complex business problems and accelerate the discovery of new ideas. H2O.ai is the trusted AI provider to more than 20,000 global organizations, millions of data scientists and over half of the Fortune 500, including AT&T, Commonwealth Bank of Australia, Citi, GlaxoSmithKline, Hitachi, Kaiser Permanente, Procter & Gamble, PayPal, PwC, Reckitt, Unilever, Goldman Sachs, NVIDIA, and Wells Fargo are not only customers and partners, but strategic investors in the company. More than 30 Kaggle Grandmasters (the community of best-in-the-world machine learning practitioners and data scientists) are makers at H2O.ai. A strong AI for Good ethos to make the world a better place and Responsible AI drive the company’s purpose. Please join our movement at www.h2o.ai. H2O.ai offers enterprise customers with multiple platforms for AI and machine learning, including the open source distributed machine learning platform H2O-3, automatic machine learning platform H2O Driverless AI, and the recently announced H2O Q, an AI platform for business users: H2O-3 is an open source, scalable and distributed in-memory AI and machine learning platform. H2O-3 also has a strong AutoML functionality and supports the most widely used statistical and machine learning algorithms including gradient boosted machines, generalized linear models, deep learning, XGBoost and more. H2O Driverless AI empowers data scientists to work on projects faster and more efficiently by using automation to accomplish tasks quickly with automatic feature engineering, model tuning, model tuning, model selection, model validation and machine learning interpretability, custom recipes, time-series and automatic deployment pipeline generation for model scoring. H2O Q is a new AI platform that provides the essential building blocks to make AI apps and will bring the power of AI to millions of business users. It delivers automatic insights and predictions for “in the moment” business questions and is ideal for data analysts, citizen data scientists and all business users.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
300-400 employees
Company's social media followers
100k-1m followers
Total funding
$250-500m
# of funding rounds
9
Latest funding date
May 12, 2023
Company
Type of company
private
Founding year
2012
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Leader
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Satisfactory
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Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
100k-1m employees
Company's social media followers
50m-60m followers
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Leader
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Satisfactory
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Automated Machine Learning platform.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
5-10 employees
Company's social media followers
1k-2k followers
Company
Type of company
private
Founding year
2016
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Challenger
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N/A
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JADBIO’s AutoML platform allows you to analyze biomedical data, fast and easy. It can yield incredible results from high dimensional feature spaces, upwards of a million, and small sample size. JADBio’s robust analytics engine can simultaneously build a predictive model, describe the performance of that model, and identify statistically-equivalent feature subsets for knowledge discovery.\\
Expand your frontiers. With JADBio’s auto-machine learning engine you can focus on what really matters: the Augmented Intelligence Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
10-20 employees
Company's social media followers
1k-2k followers
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Challenger
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N/A
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Akkio is a simple, visual, easy-to-use platform that enables anyone to supercharge everyday sales, marketing, and finance tasks with the power of AI. Train and deploy AI models in under 5 minutes. No consultants. No software to install. No sales conversations. No AI experience needed. Try free and see how AI can help grow your business.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
30-40 employees
Company's social media followers
5k-10k followers
Total funding
$10-50m
# of funding rounds
2
Latest funding date
August 1, 2023
Last funding amount
$10-50m
Company
Type of company
private
Founding year
2019
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Challenger
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N/A
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Enhencer is an AutoML Platform with a focus on practicality and transparency. It has a state-of-the-art user interface that allows building Machine Learning models with a few clicks. Enhencer presents understandable performance metrics; consequently making model performance evaluation and tuning a simple task. Also, model performance over time can be tracked in the interfaces of Enhencer.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Number of case studies
10-20 case studies
Company's number of employees
40-50 employees
Company's social media followers
5k-10k followers
Total funding
$1-5m
# of funding rounds
4
Latest funding date
April 5, 2024
Last funding amount
$1-5m
Company
Type of company
private
Founding year
2020
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Challenger
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N/A
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dotData Pioneered the AutoML 2.0 full-cycle data science automation platform. Fortune 500 organizations around the world use dotData to accelerate their ML and AI projects and deliver higher business value. dotData’s automated data science platform speeds time to value by accelerating, democratizing, augmenting and operationalizing the entire data science process, from raw business data through data and feature engineering to machine learning in production. With solutions designed to cater to the needs of both data scientists as well as citizen data scientists, dotData provides unmatched value across the entire organization. dotData’s unique AI-powered feature engineering delivers actionable business insights from relational, transactional, temporal, geo-locational, and text data. dotData has been recognized as a leader by Forrester in the 2019 New Wave for AutoML platforms. dotData has also been recognized as the “best machine learning platform” for 2019 by the AI breakthrough awards and was named an “emerging vendor to watch” by CRN in the big data space. For more information, visit www.dotdata.com, and join the conversation on Twitter and LinkedIn.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfactionMarket presence
Number of case studies
10-20 case studies
Company's number of employees
50-100 employees
Company's social media followers
3k-4k followers
Total funding
$50-100m
# of funding rounds
3
Latest funding date
May 3, 2022
Last funding amount
$10-50m
Company
Type of company
private
Founding year
2018
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Challenger
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N/A
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Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction |
“-”: AIMultiple team has not yet verified that vendor provides the specified feature. AIMultiple team focuses on feature verification for top 10 vendors.
Sources
AIMultiple uses these data sources for ranking solutions and awarding badges in AutoML software:
AutoML Leaders
According to the weighted combination of 4 metrics





What are AutoML
customer satisfaction leaders?
Taking into account the latest metrics outlined below, these are the current AutoML customer satisfaction leaders:





Which AutoML solution provides the most customer satisfaction?
AIMultiple uses product and service reviews from multiple review platforms in determining customer satisfaction.
While deciding a product's level of customer satisfaction, AIMultiple takes into account its number of reviews, how reviewers rate it and the recency of reviews.
- Number of reviews is important because it is easier to get a small number of high ratings than a high number of them.
- Recency is important as products are always evolving.
- Reviews older than 5 years are not taken into consideration
- older than 12 months have reduced impact in average ratings in line with their date of publishing.
What are AutoML
market leaders?
Taking into account the latest metrics outlined below, these are the current AutoML market leaders:





Which one has collected the most reviews?
AIMultiple uses multiple datapoints in identifying market leaders:
- Product line revenue (when available)
- Number of reviews
- Number of case studies
- Number and experience of employees
- Social media presence and engagement
What are the most mature AutoML software?
Which one has the most employees?





Which AutoML companies have the most employees?
42 employees work for a typical company in this solution category which is 19 more than the number of employees for a typical company in the average solution category.
In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 11 companies with >10 employees are offering automl software. Top 3 products are developed by companies with a total of 300k employees. The largest company in this domain is Google with more than 300,000 employees. Google provides the AutoML solution: Google Cloud AutoML
Insights
What are the most common words describing AutoML software?
This data is collected from customer reviews for all AutoML companies. The most positive word describing AutoML software is “Easy to use” that is used in 16% of the reviews. The most negative one is “Difficult” with which is used in 4% of all the AutoML reviews.
What is the average customer size?
According to customer reviews, most common company size for AutoML customers is 1,001+ employees. Customers with 1,001+ employees make up 41% of AutoML customers. For an average Machine Learning solution, customers with 1,001+ employees make up 30% of total customers.
Customer Evaluation
These scores are the average scores collected from customer reviews for all AutoML software. AutoML software are most positively evaluated in terms of "Overall" but falls behind in "Likelihood to Recommend".
What are the benefits of AutoML?
The most commonly cited benefits of AutoML are:
- Time saving
- Enhanced collaboration
- Cost saving
- Scalability
- Reduced rework
Discover all AutoML benefits
Where are AutoML vendors' HQs located?
Trends
What is the level of interest in AutoML software?
This category was searched on average for 3.6k times per month on search engines in 2024. This number has decreased to 0 in 2025. If we compare with other machine learning solutions, a typical solution was searched 3.6k times in 2024 and this decreased to 0 in 2025.
Learn more about AutoML software
AutoML is a subfield of machine learning concerned with the automation of repetitive tasks of ML processes. It offers pre-designed data analysis tools that allow businesses to obtain well-performing machine learning algorithms for accurate, low-cost, and quick predictions. Wikipedia defines AutoML as "the process of automating the end-to-end process of applying machine learning to real-world problems."
AutoML solutions aim to automate some or all steps of the machine learning process, which includes:
- Data pre-processing: While real-world data likely contain errors and often incomplete, this process transforms raw data into an understandable format. Techniques like data cleaning, data integration, data transformation, and data reduction are included in this step.
- Feature engineering: It is a method of using domain knowledge of the data to construct features that make machine learning algorithms work.
- Feature extraction: This process combines or reduces variables in the raw data to obtain useful features and reduce the amount of data to be processed.
- Feature selection: Within the raw data, there might be many features that contain irrelevant data. You can choose and use only useful features for analysis in this process.
- Algorithm selection & hyperparameter optimization: A hyperparameter is a parameter whose value is used to control the learning process. AutoML tools can choose a set of optimal hyperparameters for a learning algorithm, and even select the algorithm that works best with the given conditions.
In a world where people generate increasing amounts of data, businesses require a wide range of data science techniques to conduct accurate analyses and make careful decisions. Without these methods, organizations might be unable to understand their customers clearly, notice sales trends, and can take actions that might result in huge losses. In this environment where data science is becoming more critical for businesses, data science talent is scarce, and projects take significant time. AutoML aims to solve both problems through automation and is, therefore, being adopted by global enterprises.
Human error and bias can undermine the consistency of an organization's models and lead to less accurate predictions. AutoML allows companies to quickly adopt machine learning solutions and leverage the expertise of data scientists on human-level cognitive tasks that can not be easily automated. This increases the return on investment in data science projects and shortens the amount of time it takes to go live and generate business benefits.
AutoML solutions support companies to provide more efficient services. The main benefits can be summarized as below:
- Cost Reductions: AutoML solutions save a significant amount of time by eliminating manual parts of the analyses and providing faster deployment. With that, the productivity of machine learning processes increases. Also, AutoML reduces the demand for data scientists by democratizing machine learning.
- Improved Accuracy: As companies grow, the amount of data expands, and trends in the industry evolve. AutoML leads to better models by combining human expertise with machine precision on automatable tasks. As a consequence, all potential errors are removed, and continuously evolving algorithms increase accuracy. For this advantage, businesses can achieve a high degree of accuracy in their forecasts and increase their revenues and customer satisfaction with more accurate insights.
Businesses can automate their machine learning processes in a wide range of use cases. Mostly, companies want to boost the efficiency of their machine learning methods and reach automated insights for better data-driven decisions and forecasts. Typical use cases include:
- Fraud Detection
- Pricing
- Sales Management
You can read our AutoML case studies guide to learn more about use cases.
Although we expect AutoML solutions to grow stronger, there are still limitations that restrain AutoML from its full capacity. Here are the primary pitfalls:
- Still under development: AutoML is still a growing technology that hasn't reach its potential yet. While it mostly focuses on only supervised models, we can observe that humans beat models that are generated by AutoML solutions.
- Requires high computational power: To run machine learning processes automatically, companies need to satisfy high computing and storage requirements. Most businesses might prefer more straightforward solutions, as they might not meet them.
- Lack of explanability: Businesses look for models that are transparent and understandable. Thus, complex models wouldn't be preferred. However, AutoML models can be more complicated than manually configured models, as automated models tend to add complexity to improve results. However, there is a significant effort in this field to ensure that autoML models do not bring additional complexity.
While you can find AutoML solution providers above, we can collect them under three main categories:
- Open Source: Even secretive tech giants like Apple have released their research findings on AutoML. However, open-source tools require a user to write at least a few lines of code in Python or R to initiate processes.
- Startups: Many startups aim to provide AutoML tools that can be operated by a non-technical user. Many of these solutions also offer a visualization for greater transparency of the resulting models.
- Tech Giants: Tech giants like Google start to offer AutoML solutions for businesses. While Google Cloud AutoML is one of the first AutoML tools to be introduced by a tech giant, IBM's SPSS is one of the most common analytics software providers and offers numerous tools, like auto-classifiers.
To learn more, feel free to read our AutoML software guide.
Data scientists predict that AutoML will get better every day and allow the data-driven industries to handle their core processes efficiently. No matter in which area you're doing business, AutoML is likely to become a powerful solution that can manage the manual parts of your machine learning processes. According to a recent ODSC West 2018 talk by Randal S. Olson, Ph.D., in the next five years, AutoML solutions will:
- handle most of the data cleaning processes.
- improve the performance of deep learning algorithms.
- be more scalable, meaning that large datasets will be handled more efficiently.
- become human competitive.
- be a step towards a broader meta-learning movement.
Several best practices can be implemented to aid in AutoML processes. According to DataRobot, one of the leading vendors, the best practices of AutoML tools include the following:
- Start by collecting data: Businesses should describe the tangible result that they intend to forecast, like revenue or consumer turnover. They also need to understand that paper-based data is challenging to obtain, and they have to invest in digitalization.
- Focus on low-risk endeavors that can be completed in less than six months: Colin Priest, the vice president of DataRobot, states that any project that takes more than a year is "almost certainly doomed for failure," and ones that last longer than six months are also at high risk due to project drags. Thus, companies should seek ideas that can be delivered to the market in a shorter time.
- Beware of team silos: One primary reason for abandoned projects is that IT teams aren't informed early enough in the project's life cycle. Companies should ensure that their services can be applied alongside with the new project.
- Debunking the ‘replacement’ myth: The best types of problems to address are those that involve bringing in more customers, developing your product, boosting customer satisfaction, and optimizing production lines. At the same time, AutoML projects that are about reducing expenses or replacing staff tend to fail.