No or low-code machine learning (ML) platforms are tools that allow users to build and deploy machine learning models without needing to have extensive coding or technical skills. The idea behind these platforms is to make it easier for people with little or no coding experience to leverage the power of machine learning to solve problems and automate tasks.
Low-code ML platforms typically provide a graphical user interface (GUI) or a visual drag-and-drop interface that allows users to assemble machine learning models using pre-built components, such as algorithms and pre-processing functions. These platforms also provide libraries of pre-trained models that can be fine-tuned for specific tasks, as well as tools for training and deploying models on cloud-based or on-premises infrastructure.
By using pre-trained models, pre-configured algorithms, and templates to automatically create the code necessary to build and deploy models, no-code ML platforms go one step further and completely do away with the need for any coding at all.
The top no-code or low-code machine learning (ML) and Artificial Intelligence(AI) platforms are listed below:
H2O.ai is a low-code platform available for creating and deploying machine learning models.
DataRobot is a low-code platform for fully automating machine learning.
Google AutoML is a low-code platform for creating unique machine learning models utilising state-of-the-art Google engineering.
IBM Watson Studio is a low-code platform for creating and deploying analytical and machine learning models.
RapidMiner: A platform for creating, deploying, and managing machine learning models that requires little to no coding.
KNIME: A platform for developing and deploying low-code, open-source machine learning models.
Alteryx, a low-code platform is used to automate workflows in data science and machine learning.
Microsoft Azure Machine Learning Studio is a low-code platform for creating, deploying, and managing machine learning models is .
BigML is a platform for creating, deploying, and managing machine learning models in the cloud that requires little to no coding.
AWS SageMaker is used for creating, deploying, and managing machine learning models on Amazon Web Services.
Keep in mind that this list is not all-inclusive and that other platforms might also be relevant to you, based on your particular needs and requirements.
End Notes:
In conclusion, no-code and low-code ML platforms are designed to make it simpler and more accessible for non-technical users to integrate machine learning and artificial intelligence into their processes and applications.
