Machine Learning Platform for AI

Features

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  Data Processing

  Data Processing

  Machine Learning Platform for AI provides data preprocessing components and feature engineering components for data processing. Data preprocessing components include normalization, standardization, data sampling, and data filtering. Feature engineering components include feature conversion, feature generation, and feature importance evaluation.

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  Data Mining and Analysis

  Machine Learning Platform for AI provides statistics components, machine learning components, and network analysis components for data analysis. Statistics components include statistical analysis and data visualization analysis. Machine learning components include regression, classification, and clustering. Network analysis components include label propagation and largest connected subgraph.

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  NLP (Natural Language Processing)

  Machine Learning Platform for AI provides text processing components for NLP, including word splitting, deprecated word filtering, LDA, TF-IDF, and text summarization.

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  Architecture

  The architecture of Machine Learning Platform For AI is composed of multiple layers. From bottom to top, the layers include computing engine, distributed computing architecture, algorithm component , and service application. The computing engine layer of Machine Learning Platform For AI relies on the Apsara distributed computing system. This allows Machine Learning Platform For AI to concurrently compute EB-level data.

  
  

  The distributed architecture computing layer of Machine Learning Platform For AI provides support for multiple distributed computing architectures, such as MPI, MR, and GRAPH. The algorithm component layer provides support for more than one hundred machine learning algorithms.

  
  

  By using its own algorithms, Machine Learning Platform For AI can support multiple service scenarios such as product recommendation, financial risk management, and advertising at the service application layer.