Apache Spark MLlib is a machine learning library which runs on top of Spark core. It supports distributed computing and it can scale vertically and horizontally. It offers APIs for Java, Scala, Python, R and SQL.
It provides tools such as:
- ML Algorithms: common learning algorithms such as classification, regression, clustering, and collaborative filtering
- Featurization: feature extraction and selection, transformation, dimensionality reduction
- Pipelines: tools for constructing, evaluating, and tuning ML pipelines
- Persistence: saving and loading of algorithms, models and pipelines
- Utilities: linear algebra, statistics, data handling, etc.
Introduction and principles of MLflow With increasingly cheaper computing power and storage and at the same time increasing data collection in all walks of life, many companies integrated Data Science…
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With the arrival of Hadoop 3, YARN offer more flexibility in resource management. It is now possible to perform Deep Learning analysis on GPUs with specific development environments, leveraging…
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Let’s dive into the new features offered by the 2.3 distribution of Apache Spark. This article is a composition of the following talks seen at the DataWorks Summit 2018 and additional research: Apache…
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