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Product Recommendation
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Customer Grouping
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Anomaly Detection
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Predictive Maintenance
Product Recommendation
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.
Related Information
Recommended Algorithms
Random Decision Forest Classification (RDFC) and Gradient Boosted Tree Classification (GBTC)
Scenarios
Wealth management recommendation and vehicle price prediction
Customer Grouping
You can scientifically group customers by data mining and formulate strategies based on customer group characteristics to provide appropriate products and develop target marketing activities, thereby achieving commercial benefits.
Related Information
Recommended Algorithms
K-Means
Scenarios
Retailers' customer grouping
Anomaly Detection
You can use an automatic network detection system to predict suspicious traffic or faulty devices according to real-time traffic analysis.
Related Information
Recommended Algorithms
PCA-Based Anomaly Detection and Isolation Forest (IF)
Scenarios
Network intrusion detection
Predictive Maintenance
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.
Related Information
Recommended Algorithms
Logistic Regression (LR) and Gradient Boosted Tree Regression (GBTR)
Scenarios
Automobile manufacturing and maintenance
Functions
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Drag-and-Drop Workflow
MLS provides various nodes that facilitate drag-and-drop creation of modeling workflows.
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Visualization
Data and model visualization functions allow data and machine learning models to be visualized in real time.
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Notebook
MLS provides Notebook that is compatible with third-party development packages and enables data visualization.
Drag-and-drop workflow creation
Simplified and visualized modeling process design allows you to create service analysis and modeling processes without programming.
Quick response
MLS leverages visualization to achieve fast iteration and convergence so that models can be rapidly deployed in offline production environments.
Data visualization
Data exploration and analysis results can be visualized in various graphs, improving data exploration efficiency.
Model visualization
Trained models and evaluation results are visualized so that they are easy to understand.
Notebook framework
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.
Powerful data analysis capabilities
The data analysis process covers data exploration, feature engineering, data modeling, and data visualization.
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Model Lifecycle Management
MLS supports model lifecycle management covering construction, prediction, deployment, and scheduling.
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Preset Machine Learning Algorithms
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.
Visualized model lifecycle management
Models of a service are managed according to version. You can manage model and service lifecycles on a visualized management interface.
Industry model standards
Industry-standard PMML files can be imported and exported to be seamlessly integrated with other machine learning software.
Distributed machine learning algorithm library
Large-scale distributed computing increases computing efficiency, allowing models to be trained on larger data sets to increase the accuracy of results.
Automatic data exploration and parameter tuning
Data exploration extracts data types and statistics from raw data. Parameters are automatically tuned for logistic regression and linear regression.
