Weekly BigData & ML Roundup – Mar. 23, 2017

LLVM for Data Processing
When Apache Spark launched Tungsten, there was a hint of incorporating LLVM into data processing pipeline to make use of modern CPU features and its superb machine code for performance boost.

LLVM appears again with Weld, a new code generation project for data analytics. The project claims that Tensorflow, Spark, and Numpy can be accelerated up to 30x with just few operations of Weld!

Interestingly, Matei Zaharia is in its contributor list.

Examples

Algorithmic Trading Pipeline
Algorithmic Trading Pipeline for Online Betting Markets

Blackbird
Blackbird Bitcoin Arbitrage: a long/short market-neutral strategy

Toolsets

Weld
Weld is a runtime and language for accelerating data analytics frameworks

bq-utils
Utitilties for BigQuery such as downloading table / query to csv/ndjson/excel/gsheet or new table using iterators for a low memory footprint.

Wiki2Vec
Generating Vectors for DBpedia Entities via Word2Vec and Wikipedia Dumps

ETL Starter Kit
Extract, Transform, Load (ETL) refers to a process in database usage and especially in data warehousing.

Facebook Visdom
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.

Vivitics Node (VNode)
Vivitics Node (VNode): A workbench for Data Science powered by Jupyter and Docker

Models

Mozilla DeepSpeech
A TensorFlow implementation of Baidu’s DeepSpeech architecture

DiscoGAN
Official implementation of “Learning to Discover Cross-Domain Relations with Generative Adversarial Networks”

Libraries

Redis-ML
Machine Learning Model Server

UC Berkeley Ray
An experimental distributed execution engine


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