What are our data sources?
We use the data sources on the side for ranking solutions and awarding badges in automl software category. Our data sources in automl software category include the items to the below;
Automated Machine Learning (AutoML) software, also known as AutoML services/tools, enables data scientists and machine learning engineers as well as non-technical users, to automatically build scalable machine learning models.
Most AutoML software achieve this by auto-analyzing data and selecting algorithms models based on insights gained from data analysis. These models are trained, tested and refined (hyperparameter tuning) on a subset of the available data using various methodologies. Finally, models with the best performance are shared with the end-user.
Most AutoML software allow users to trade-off between complexity and performance. Therefore users have the chance to build complex models with high performance or less complex models, explainable models that offer slightly inferior performance.
To be categorized as autoML software, a product must be able to:
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We use the data sources on the side for ranking solutions and awarding badges in automl software category. Our data sources in automl software category include the items to the below;
review websites
social media websites
search engine data for branded queries
According to the weighted combination of 7 data sources
DataRobot
Dataiku
H2O
Google Cloud AutoML
Enhencer
Taking into account the latest metrics outlined below, these are the current automl software market leaders. Market leaders are not the overall leaders since market leadership doesn’t take into account growth rate.
DataRobot
Dataiku
H2O
Google Cloud AutoML
Akkio
37 employees work for a typical company in this solution category which is 14 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. 7 companies with >10 employees are offering automl software. Top 3 products are developed by companies with a total of 200k employees. The largest company building automl software is Google with more than 200,000 employees. Google provides the automl software: Google Cloud AutoML
Taking into account the latest metrics outlined below, these are the fastest growing solutions:
DataRobot
H2O
Dataiku
Google Cloud AutoML
Enhencer
We have analyzed reviews published in the last months. These were published in 4 review platforms as well as vendor websites where the vendor had provided a testimonial from a client whom we could connect to a real person.
These solutions have the best combination of high ratings from reviews and number of reviews when we take into account all their recent reviews.
We analyzed 81 AutoML case studies and found that these products are the top contributors:
This data is collected from customer reviews for all AutoML software companies. The most positive word describing AutoML software is “Easy to use” that is used in 7% of the reviews. The most negative one is “Difficult” with which is used in 2% of all the AutoML software reviews.
According to customer reviews, most common company size for automl software customers is 1,001+ employees. Customers with 1,001+ employees make up 30% of automl software customers. For an average Machine Learning solution, customers with 1,001+ employees make up 30% of total customers.
These scores are the average scores collected from customer reviews for all AutoML software. AutoML software is most positively evaluated in terms of "Overall" but falls behind in "Likelihood to Recommend".
The most commonly cited benefits of AutoML are:
This category was searched on average for 1k times per month on search engines in 2023. This number has increased to 1.1k in 2024. If we compare with other machine learning solutions, a typical solution was searched 995 times in 2023 and this increased to 1k in 2024.
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:
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:
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:
Although we expect AutoML solutions to grow stronger, there are still limitations that restrain AutoML from its full capacity. Here are the primary pitfalls:
While you can find AutoML solution providers above, we can collect them under three main categories:
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:
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: