Scales efficient language processing with open-source accessibility.
357M visits/mo+1%#2 Coding & Development
Automated ML
Create ML models easily, accelerate development with pre-built models and templates, and monitor performance with analytics tools.
14KMonthly visits
19Popularity
About Automated ML
Automated ML is an AI-based platform designed to simplify and speed up the machine learning workflow. It provides users with the tools to quickly and easily build, deploy, and manage powerful machine learning models for any application. Automated ML’s intuitive drag-and-drop interface allows users to quickly create sophisticated models with just a few clicks. It also includes a variety of pre-built models and templates tailored to specific applications, so users can get up and running quickly. Automated ML makes it easy for developers and data scientists to get their machine learning projects up and running in no time. The platform also includes a powerful set of analytics and monitoring tools to ensure that models are running optimally. With Automated ML, users can quickly and easily build powerful machine learning models without the need for deep technical expertise.
Key features
- Create powerful ML models with drag-and-drop interface
- Accelerate development with pre-built models and templates
- Monitor performance with powerful analytics tools
Use cases
- Build, deploy, and manage machine learning models for various applications
- Simplify and speed up the machine learning workflow
- Create sophisticated models without deep technical expertise
Pros
- Focuses on core AutoML research areas such as hyperparameter optimization, neural architecture search, and meta-learning
- Develops open-source tools and benchmarks to standardize and advance AutoML practices
- Collaborates across multiple academic institutions to drive innovation in machine learning automation
- Provides resources for both technical and non-technical users to improve accessibility of machine learning
- Offers educational materials, including MOOCs and tutorials, to support learning in AutoML
Cons
- Primarily academic and research-oriented, which may limit direct applicability for commercial users
- Lacks a unified commercial platform, focusing instead on tools and frameworks for developers
- Documentation and resources are more technical, potentially posing challenges for beginners
Frequently asked questions about Automated ML
What is Automated ML (AutoML)?
Automated ML, or AutoML, refers to methods and processes that make machine learning more accessible, improve the efficiency of ML systems, and accelerate AI application development. It aims to automate manual tasks typically performed by human ML experts.
Who is Automated ML designed for?
AutoML is designed for users ranging from non-ML experts to experienced researchers and developers. It addresses the demand for off-the-shelf machine learning methods that can be used easily without deep technical expertise.
What research areas does Automated ML cover?
AutoML covers research areas such as hyperparameter optimization, neural architecture search, meta-learning, explainable AutoML, and dynamic algorithm configuration. It also includes tools for reinforcement learning, healthcare, and efficient ML systems.
What tools does Automated ML provide?
AutoML provides open-source tools like SMAC3, Auto-PyTorch, NASLib, and others for tasks such as hyperparameter optimization, neural architecture search, and performance prediction. These tools are developed by academic research groups at the University of Freiburg, Leibniz University Hannover, and the University of Tübingen.
How can I get started with Automated ML?
Users can explore AutoML through its open-source tools, research publications, tutorials, and benchmark resources available on the website. The platform also offers educational materials like MOOCs and workshops to help users learn and apply AutoML techniques.
Does Automated ML offer any educational resources?
Yes, AutoML provides educational resources including MOOCs, tutorials, invited talks, workshops, and AutoML schools. These resources are designed to help users understand and apply AutoML methods in their projects.
Automated ML traffic & engagement
automl.org · third-party estimates from Similarweb
Data as of Sep 2026Full AI website ranking →
Monthly visits · Sep 202614K-46.2% MoM12-month high 39K (Jun 2026)
Rank among AI websites#4,056▼ 679 vs last monthof 8,652 tools on aitoolnavio™
Top marketUnited States24.1%Top 5 countries bring 63% of visits
Average visit12sTime on site per visit
Pages per visit1.81Average pages viewed
Bounce rate39%Lower than 50% of AI sites
Similarweb global rank#1,708,010#1,626,305 in United States
Traffic trend
Last 4 months · grows by one month every month
- Jun 202639K
- Jul 202628K−27.8%
- Aug 202626K−8.6%
- Sep 202614K−46.2%
Top countries
Share of visits · estimated visits
Other countries: 37.2%
Compared with similar tools
Coding & Development · Sep 2026
Traffic figures are third-party estimates, good for scale and trends; they can differ from a company's own numbers. Updated monthly.
