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apache/spark

Apache Spark - A unified analytics engine for large-scale data processing

big-data jdbc r scala python spark java sql

29924 23914 329

eclipse/deeplearning4j

Model import deployment framework for retraining models (pytorch, tensorflow,keras) deploying in JVM Micro service environments, mobile devices, iot, and Apache Spark

Scala (JVM): 2.10 2.11 2.12

neural-nets linear-algebra deeplearning4j artificial-intelligence java deeplearning hadoop intellij scala python gpu clojure spark dl4j matrix-library

12063 4893 49

deeplearning4j/deeplearning4j

Eclipse Deeplearning4j, ND4J, DataVec and more - deep learning & linear algebra for Java/Scala with GPUs + Spark

Scala (JVM): 2.10 2.11

neural-nets linear-algebra deeplearning4j artificial-intelligence java deeplearning hadoop intellij scala python gpu clojure spark dl4j matrix-library

11130 4735 15

catboost/catboost

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Scala (JVM): 2.11 2.12

gbdt tutorial gpu-computing gbm big-data categorical-features cuda catboost data-science python gpu data-mining kaggle coreml r machine-learning gradient-boosting decision-trees

5885 898 165

h2oai/h2o-3

Open Source Fast Scalable Machine Learning Platform For Smarter Applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Scala (JVM): 2.10 2.11

naive-bayes multi-threading data-science python gpu spark ensemble-learning random-forest pca java gbm automl big-data h2o hadoop deep-learning r machine-learning opensource distributed

4777 1723 132

pantsbuild/pants

The Pants Build System

Scala (JVM): 2.10 2.11 2.12

javascript java scala rust build-tools build-system monorepo python build-tool golang

1290 363 176

combust/mleap

MLeap: Deploy ML Pipelines to Production

Scala (JVM): 2.10 2.11 2.12

tensorflow scikit-learn scala transformers data-pipelines python spark

1250 278 56

apache/incubator-sedona

A cluster computing framework for processing large-scale geospatial data

Scala (JVM): 2.11 2.12

cluster-computing java scala spatial-sql spatial-query python geospatial spatial-analysis

906 412 41

cdapio/cdap

An open source framework for building data analytic applications.

Scala (JVM): 2.11

mapreduce cdap java unified spark-streaming spark dataset platform middleware java-8 integration python

532 267 120

shadaj/scalapy

Use the world of Python from the comfort of Scala!

Scala (JVM): 2.11 2.12 2.13

Scala-Native: 0.3 0.4

scala python scala-native

370 29 9

caskdata/cdap

An open source framework for building data analytic applications.

Scala (JVM): 2.11

mapreduce cdap java unified spark-streaming java-8 spark dataset platform integration python

281 143 80

locationtech-labs/geopyspark

GeoTrellis for PySpark

Scala (JVM): 2.11

geotrellis big-data python tile-server geospatial spark

95 29 7

mrdimosthenis/synapses

A lightweight library for neural networks that runs anywhere

Scala (JVM): 2.13

deep-learning machine-learning scala functional-programming python neural-network lightweight javascript csharp fsharp java cross-platform

33 2 3

isarn/isarn-sketches-spark

Routines and data structures for using isarn-sketches idiomatically in Apache Spark

Scala (JVM): 2.10 2.11 2.12

datasets spark-ml t-digest feature-importance dataset aggregator pyspark apache-spark scala python data-sketches data-sketching dataframe variable-importance sketching-algorithm dataframes udaf spark

23 6 2

liquidsvm/liquidsvm

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 (JVM): 2.11