This library helps reduce the time it takes to develop high-performance data science applications. Enable applications to make better predictions faster and analyze larger data sets with available compute resources.
- Includes highly optimized machine learning and analytics functions
- Simultaneously ingests data and computes results for highest throughput performance
- Supports batch, streaming, and distribution use models to meet a range of application needs
- Use the same API for application development on multiple operating systems
Note This library supports Python*. To access a Python interface for the Intel® Data Analytics Acceleration Library (Intel® DAAL) high-speed algorithms, use the daal4py that is included in the Intel® Distribution for Python*.
What’s New in the 2019 Edition
- Logistic regression—the most widely used classification algorithm
- Extended gradient boosting functionality for inexact split calculations
- User-defined callback canceling to provide greater flexibility
- User-defined data modification procedure to support a wide range of feature extraction and transformation techniques
Fits in the data analytics ecosystem
This library addresses all stages of the data analytics pipeline: preprocessing, transformation, analysis, modeling, validation, and decision-making.
About the Library
Intel DAAL outperforms other solutions for developers and data scientists. This benchmark compares performance of the XGBoost implementation in Intel DAAL to an XGBoost open source project. The y-axis shows a speedup factor of two to twelve times for four representative classification and regression test cases.