Ease of Use
MLS provides a graphical interface that supports drag-and-drop operations, allowing you to easily create workflows for data modeling, analysis, prediction, and visualization.
MLS offers Notebook and supports various open source modeling languages, such as Python and more.
MLS provides abundant machine learning algorithms for you to import and process data, as well as train, evaluate, and export models, covering end-to-end prediction and analysis services.
MLS provides a series of one-stop machine learning applications covering feature engineering, machine learning algorithms, modeling, prediction, and model lifecycle management.
MLS recommends personalized services to customers according to their attributes and behavior features, such as age, gender, work type, marital status, education background, and financial status.
Random Decision Forest Classification (RDFC) and Gradient Boosted Tree Classification (GBTC)
Wealth management recommendation and vehicle price prediction
You can create a prediction model for devices and provide preventive maintenance suggestions and plans to shorten downtime and reduce the probability of faults, thereby improving efficiency and reducing costs.
Logistic Regression (LR) and Gradient Boosted Tree Regression (GBTR)
Automobile manufacturing and maintenance
MLS provides various nodes that facilitate drag-and-drop creation of modeling workflows.
Simplified and visualized modeling process design allows you to create service analysis and modeling processes without programming.
MLS leverages visualization to achieve fast iteration and convergence so that models can be rapidly deployed in offline production environments.
Data and model visualization functions allow data and machine learning models to be visualized in real time.
Data exploration and analysis results can be visualized in various graphs, improving data exploration efficiency.
Trained models and evaluation results are visualized so that they are easy to understand.
MLS provides Notebook that is compatible with third-party development packages and enables data visualization.
The Notebook framework serves as a browser and a unified entry point for data analysis. It can be decoupled from the computing framework of the underlying big data platform and is compatible with open source Python libraries.
The data analysis process covers data exploration, feature engineering, data modeling, and data visualization.
MLS supports model lifecycle management covering construction, prediction, deployment, and scheduling.
Models of a service are managed according to version. You can manage model and service lifecycles on a visualized management interface.
Industry-standard PMML files can be imported and exported to be seamlessly integrated with other machine learning software.
Numerous preset algorithms help you import and process data, as well as train, evaluate, and export models, covering end-to-end prediction and analysis services.
Large-scale distributed computing increases computing efficiency, allowing models to be trained on larger data sets to increase the accuracy of results.
Data exploration extracts data types and statistics from raw data. Parameters are automatically tuned for logistic regression and linear regression.