Machine Learning Service

Machine Learning Service (MLS) helps you quickly find data patterns to construct prediction models through machine learning technologies and deploy these models as prediction and analysis solutions.

Pricing of the standard edition for data analysis and modeling starts from ¥0.53/hour

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Product Advantages
  • 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.

  • Openness

    MLS offers Notebook and supports various open source modeling languages, such as Python and more.

  • Abundant Algorithms

    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.

  • One-Stop

    MLS provides a series of one-stop machine learning applications covering feature engineering, machine learning algorithms, modeling, prediction, and model lifecycle management.

Application Scenarios
  • Product Recommendation

  • Customer Grouping

  • Anomaly Detection

  • Predictive Maintenance

Product Recommendation

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

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

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

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

  • Drag-and-Drop Workflow

    MLS provides various nodes that facilitate drag-and-drop creation of modeling workflows.

  • Visualization

    Data and model visualization functions allow data and machine learning models to be visualized in real time.

  • Notebook

    MLS provides Notebook that is compatible with third-party development packages and enables data visualization.

Drag-and-Drop Workflow

  • 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.

Visualization

  • 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

  • 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.

  • Model Lifecycle Management

    MLS supports model lifecycle management covering construction, prediction, deployment, and scheduling.

  • 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.

Model Lifecycle Management

  • 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.

Preset Machine Learning Algorithms

  • 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.

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