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itspawanbhardwaj/spark-fuzzy-matching 1.0.1
Fuzzy matching function in spark (https://spark-packages.org/package/itspawanbhardwaj/spark-fuzzy-matching)
Scala versions: 2.11 -
whylabs/whylogs-java 0.1.3
Profile and monitor your ML data pipeline end-to-end
Scala versions: 2.12 -
isarn/isarn-sketches-spark 0.6.0-sp3.2
Routines and data structures for using isarn-sketches idiomatically in Apache Spark
Scala versions: 2.12 -
astrolabsoftware/spark3d 0.3.1
Spark extension for processing large-scale 3D data sets: Astrophysics, High Energy Physics, Meteorology, …
Scala versions: 2.11 -
data-tools/big-data-types 1.3.4
A library to transform Scala product types and Schemes from different systems into other Schemes. Any implemented type automatically gets methods to convert it into the rest of the types and vice versa. E.g: a Spark Schema can be transformed into a BigQuery table.
Scala versions: 3.x 2.13 2.12 -
tupol/spark-tools 0.4.1
Executable Apache Spark Tools: Format Converter & SQL Processor
Scala versions: 2.12 2.11 -
coxautomotivedatasolutions/vegalite4s 0.4
Vega-Lite4s is a small library over the comprehensive Vega-Lite Javascript visualisation library, allowing you to create beautiful Vega-Lite visualisations in Scala
Scala versions: 2.12 2.11 -
emcecs/spark-ecs-connector 1.4.2
[Archived] ArchiveECS connector for Apache Spark
Scala versions: 2.11 -
exasol/spark-connector 1.1.0
A connector for Apache Spark to access Exasol
Scala versions: 2.12 -
flipkart-incubator/spark-transformers 0.4.0
Spark-Transformers: Library for exporting Apache Spark MLLIB models to use them in any Java application with no other dependencies.
Scala versions: 2.11 2.10 -
salmon-brain/dead-salmon-brain 0.0.8
Apache Spark based framework for analysis A/B experiments
Scala versions: 2.12 -
liquidsvm/liquidsvm 0.6.0
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
Scala versions: 2.11 -
mlflow/mlflow 2.16.0
Open source platform for the machine learning lifecycle
Scala versions: 2.13 2.12 -
chitralverma/sparkml-extensions 0.1
Scala versions: 2.11