Popular No-Code Machine Learning Platforms

1. Google Cloud AutoML:

• Google’s AutoML suite contains tools that can be used for modeling even complex machine-learning issues with just minimum coding.
• With the help of AutoML Vision, Natural Language, and Tables, end-users can design image recognition, text sorting, and structured data analysis models accordingly.
• The platform allows for easy dataset importation, model training, and evaluation, thereby being suitable for users with no technical background.

2. Microsoft Azure Machine Learning Studio:

• Azure user interface of the drag-and-drop type makes it possible to build, train, and deploy ML models with almost zero human effort.
• New code-free AI tools allow users of the platform to develop predictive analytics, anomaly detection, and recommendation systems with a large library of pre-built modules and algorithms.
• Azure Machine Learning Studio comes with a streamlined integration to other Microsoft services, which is instrumental in overall AI solutions developed within the Azure environment.

3. IBM Watson Studio:

• IBM (Watson Study) is the collaborative workspace for data scientists and business analysts to co-create AI projects.
• One of the strengths of the platform is its autoAI capabilities, where the users can produce and optimize machine learning models without human involvement.
• Watson’s studio is empowering a wide variety of states, like predictive maintenance, fraud detection, and customer churn analysis; which makes it suitable for multiple industries and fields.

4. H2O.ai Driverless AI:

• The H2O.ai Driverless AI solution comprises an end-to-end ML pipeline automation starting with data preprocessing, model training, to model deployment.
• Additionally, users can also be able to upload datasets and Driverless AI would automatically construct feature engineering, model selection, and hyperparameter tuning pipelines.
• Interpretability functions build an understanding of model predictions and suggestions for improving model performance and enhancing users’ knowledge and ability to take informed actions.

5. DataRobot:

• DataRobot provides a no-code AI platform to users whereby unskilled users can build and deploy AI models at a large scale.
• DataRobot with its automated feature engineering and model selection enables the model building process for regression, classification and time series forecasting tasks to be as simple as it can get.

6. Akkio:

• Akkio is an ML no code platform that helps its customers create and deploy predictive models without writing a single line of code.
• It provides lots of templates and pre-defined processes on the most common problems such as segmentation of customers , churn prediction , lead scoring and personalized recommendations .
• Mutually, the Akkio interface with its smooth usage gives the user ability to get data in, train models and see the predictions easily, which is not limited just to the users with high tech understanding.

7. Zoho Creator:

• Zoho Creator is a low-code application development instrument that comprises of machine learning element.
• The Zoho Creator provides the drag and drop interface for users to be able to create their own business applications and integrate machine models of learning for analytical tasks such as predictive analysis, sentiment analysis and recommendation engines.
• Zoho Creator’s native embed capability into other Zoho applications such as CRM aids users to optimize their workflows and to a large extent automate processes without much coding.

8. Bubble:

• Bubble is a no-code web development environment that allows users to develop the applications and deploy their web applications without coding.
• Although the technology does not concentrate on machine learning, there are integrations with various third-party APIs such as Google Cloud Vision or Natural Language Understanding by IBM Watson which can be used to enrich the application with machine learning functionality.
• Bubble’s very intuitive interface and visual editor allows users to make lasting web applications with a lot of advanced features, including AI functionalities.

In the context of rapidly changing technologies, AI and ML are very important tools that drive innovation in many sectors. Nevertheless, this traditional way of programming AI is usually very complex and demands specific skills, which, in turn, presents obstacles for individuals and businesses willing to apply these technologies. “No-code machine learning”—this revolutionary approach provides non-techies with an opportunity to create and deploy ML models without any knowledge of coding. This article discusses the No-Code Machine Learning Concept, unveiling its Role, Function, and Implications for the AI development of the future.