HPCC is a proven and battle-tested platform for manipulating, transforming, querying and data warehousing Big Data.
  It supports two type of configuration. Thor is responsible for consuming vast amounts of data, transforming, linking and indexing that data. It functions as a distributed file system with parallel processing power spread across the nodes. Roxie, the Data Delivery Engine, provides separate high-performance online query processing and data warehouse capabilities.

HPCC System - Hadoop alternative

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
 
Use Keras if you need deep learning that 
<ul>
<li>Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).</li>
<li>Supports both convolutional networks and recurrent networks, as well as combinations of the two.</li>
<li>Runs seamlessly on CPU and GPU.</li>
</ul>

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

keras - Deep Learning library for Python. Runs on TensorFlow, Theano, or CNTK.

Vespa is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time. Vespa is serving platform for Yahoo.com, Yahoo News, Yahoo Sports, Yahoo Finance, Yahoo Gemini, Flickr.
 Queries can use both structured filters and unstructured text search to select data. All the matching data is then ranked according to a ranking function - typically machine learned - to implement such use cases as search relevance, recommendation, targeting and personalization.
 
Vespa is scalable. System sizes up to hundreds of nodes handling tens of billions of documents are not uncommon, and no harder to set up and modify than single node systems. Since all system components, as well as stored data is redundant and self-correcting, hardware failures are not operational emergencies and can be handled by re-adding capacity when convenient.
Its feature include:
<ul>
<li>Text search - Combine structured query and text search to select data</li>
<li>Advanced Ranking - Machine learned ranking</li>
<li>Aggregation</li>
<li>Elastic, Scalable</li>
<li>High Availability</li>
<li>Auto repair data corruption</li>
<li>Simple HTTP API interface&nbsp;</li>
<li>lot more...</li>
</ul>

Vespa is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time. Vespa is serving platform for Yahoo.com, Yahoo News, Yahoo Sports, Yahoo Finance, Yahoo Gemini, Flickr.

Vespa - Yahoo's big data serving engine

Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. With advanced data structures and algorithms, Smile delivers state-of-art performance.Smile covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc.

Smile - Statistical Machine Intelligence & Learning Engine

The Oryx open source project provides infrastructure for lambda-architecture applications on top of Spark, Spark Streaming and Kafka. On this, it provides further support for real-time, large scale machine learning, and end-to-end applications of this support for common machine learning use cases, like recommendations, clustering, classification and regression.
  

Oryx 2 - Lambda architecture on Apache Spark, Apache Kafka for real-time large scale machine learning

MapD Core is an in-memory, column store, SQL relational database that was designed from the ground up to run on GPUs. MapD Core is the foundational element of a larger data exploration platform that emphasizes speed at scale. By taking advantage of the parallel processing power of the hardware, MapD Core can query billions of rows in milliseconds. Furthermore, by using the graphics pipelines of GPUs, MapD Core can render graphics directly from the server.
 
MapD can operate as a standalone database using the command line or its administration console. MapD can also output to its native interface, MapD Immerse, or to third-party software such as Tableau, Qlik, or Birst, using a variety of connectors. 
 
Within a single server, MapD Core can handle datasets 1.5-3TB in raw size in GPU RAM, or 10-15TB in CPU RAM. In distributed deployments, that number scales linearly.

 MapD Core is an in-memory, column store, SQL relational database that was designed from the ground up to run on GPUs. MapD Core is the foundational element of a larger data exploration platform that emphasizes speed at scale. By taking advantage of the parallel processing power of the hardware, MapD Core can query billions of rows in milliseconds. Furthermore, by using the graphics pipelines of GPUs, MapD Core can render graphics directly from the server.

MapD - The MapD Core database

Zipline is a Pythonic algorithmic trading library. It is an event-driven system that supports both backtesting and live-trading. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies.Note: Installing Zipline via pip is slightly more involved than the average Python package. Simply running pip install zipline will likely fail if you've never installed any scientific Python packages before.

<ul>
<li>Ease of use: Zipline tries to get out of your way so that you can
focus on algorithm development. See below for a code example.</li>
<li>Zipline comes "batteries included" as many common statistics like
moving average and linear regression can be readily accessed from
within a user-written algorithm.</li>
<li>Input of historical data and output of performance statistics are
based on Pandas DataFrames to integrate nicely into the existing
PyData eco-system.</li>
<li>Statistic and machine learning libraries like matplotlib, scipy,
statsmodels, and sklearn support development, analysis, and
visualization of state-of-the-art trading systems.</li>
</ul>

Zipline is a Pythonic algorithmic trading library. It is an event-driven system that supports both backtesting and live-trading. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies.Note: Installing Zipline via pip is slightly more involved than the average Python package. Simply running pip install zipline will likely fail if you've never installed any scientific Python packages before.

Zipline - A Pythonic Algorithmic Trading Library

scikit-learn is a Python module for machine learning built on top of SciPy. It is simple and efficient tools for data mining and data analysis. It supports automatic classification, clustering, model selection, pre processing and lot more.

Scikit Learn - Machine Learning in Python

MNN is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models, and has industry leading performance for inference and training on-device. At present, MNN has been integrated in more than 20 apps of Alibaba Inc, such as Taobao, Tmall, Youku, Dingtalk, Xianyu and etc., covering more than 70 usage scenarios such as live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity distribution, security risk control. In addition, MNN is also used on embedded devices, such as IoT.

MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba

The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.
 
After merging with the AForge.NET project, the framework now offers a unified API for learning/training machine learning models that is both easy to use and extensible. 
 
It supports Vector Machines , Logistic Regression , Decision Trees , Neural Networks , Deep Learning (Deep Neural Networks), Levenberg-Marquardt with Bayesian Regularization , Restricted Boltzmann Machines , Sequence classification , Hidden Markov Classifiers and Hidden Conditional Random Fields, Multiple linear regression , Multivariate linear regression , polynomial regression , logarithmic regression.
 
 Clustering algorithms like K-Means , K-Modes , Mean-Shift , Gaussian Mixture Models , Binary Split, Image processing, Load, parse, save, filter and transform audio signals, Real-time face detection and tracking , as well as general methods for detecting , tracking and transforming objects in image streams and lot more.

The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

Accord.NET - Machine learning, Computer vision, Statistics and general scientific computing for .NET

Project DeepSpeech is an open source Speech-To-Text engine. It uses a model trained by machine learning techniques, based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow project to make the implementation easier.

DeepSpeech - A TensorFlow implementation of Baidu's DeepSpeech architecture

Winds is an Open Source app powered by Stream that gives you a new and personalized way to listen, read and share your favorite content. It is a beautiful RSS reader built using React, Redux, Sails, Node 7 and Stream (getstream.io). Showcases personalized feeds (using machine learning similar to Facebook, Flipboard, Etsy, and Quora - powered by the getstream.io API).

Winds - A Personalized RSS Reader

TensorFlow is a library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. 

TensorFlow - Artificial Intelligence Library from Google

 mlpack is an intuitive, fast, and flexible C++ machine learning library with bindings to other languages. It is meant to be a machine learning analog to LAPACK, and aims to implement a wide array of machine learning methods and functions as a "swiss army knife" for machine learning researchers. In addition to its powerful C++ interface, mlpack also provides command-line programs and Python bindings. 

mlpack - A scalable C++ machine learning library

The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Kubeflow is a platform for data scientists who want to build and experiment with ML pipelines. Kubeflow is also for ML engineers and operational teams who want to deploy ML systems to various environments for development, testing, and production-level serving.

kubeflow - Machine Learning Toolkit for Kubernetes

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