What is SaCa RealRec?

SaCa RealRec is an all-in-one machine learning and predictive analysis service platform based on a big data distributed processing framework. It offers visual feature analysis of the whole process, model construction evaluation, and deployment capabilities. This platform reduces the cost of artificial intelligence adoption in enterprises, helping businesses enhance their capability and efficiency in building intelligent applications.

Data collection

The platform supports data access from all major databases, file systems, and text formats. It also integrates with big data ecosystem technologies such as HDFS, HBase, Hive, and more.

Feature analysis

By utilizing various data cleansing, transformation, and dimension reduction algorithms, it effectively supports feature extraction and reuse for different machine models, significantly reducing the development cost of feature engineering.

Model training

The platform offers a large-scale machine learning algorithm library based on distributed computing frameworks. It also provides whole process visual iterative model training, assisting data scientists in building high-performance mining models.

Evaluation and assessment

The platform supports both online and offline model evaluation methods, streamlining model assessment tasks. The visual presentation of evaluation results provides clear insights, ensuring that the trained models align better with business problems.

Application deployment

Enabling deployment of applications through cloud service REST interfaces or on-site POJO applications, it establishes a smooth bridge between big data science teams and data engineering teams, ensuring agile development of intelligent applications.

Application scenarios of SaCa RealRec

Risk and fraud analysis

The platform leverages machine intelligence technology to extract and analyze information from multiple sources of data, learning transaction patterns, identifying characteristics of cross-channel abnormal transactions, and dynamically evaluating transaction safety indices. This enables real-time prevention of abnormal transactions.

Public security illegal crime analysis

Utilizing various machine learning algorithms, the platform constructs models for identifying criminal suspects, predicting individuals potentially involved in criminal activities, and providing auxiliary decision-making for law enforcement agencies in combating criminal activities.

Education and teaching

The platform assists universities in creating detailed educational materials and client cases, provides comprehensive artificial intelligence education and teaching solutions, and offers all-round supports to universities for the establishment of big data talent teams.

Why choose SaCa RealRec?

Rapid processing of big data

Utilizing a distributed data processing approach, the platform provides robust support for the efficiency of big data processing. This achieves second latency from data generation to computation result generation, offering near real-time analysis and mining capabilities.

Precision analysis and in-depth mining

The platform supports the construction of mathematical models for massive data, and enables intelligent model construction using various functional languages (DSL, SQL, R, etc.), ensuring both the depth of mining and the accuracy of predictive analysis.

Real-time analysis of multi-dimensional features

The platform offers data-driven visualization of real-time interactive query analysis results, assisting data scientists in subsequent stages of feature engineering based on the analysis results.

Whole process automated model construction

The system automatically searches for the optimal algorithm model and parameter configuration, significantly reducing the difficulty of using data mining tools. This enables even non-technical business professionals to build efficient intelligent models tailored to their own business needs.

Industry applications

Data science education solution

The platform provides comprehensive data science education services for universities.

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