Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling. 
 
Probability is the mathematics of reasoning under uncertainty, much as calculus is the mathematics for reasoning about rates of change. Models built in the language of probability can capture complex reasoning, know what they do not know, and uncover structure in data without supervision. Further, probability provides a way for human experts to provide knowledge to AI systems in the form of a priori beliefs.
 
Specifying probabilistic models directly can be cumbersome and implementing them can be very error-prone. Probabilistic programming languages (PPLs) solve these problems by marrying probability with the representational power of programming languages. A probabilistic program is a mix of ordinary deterministic computation and randomly sampled values; this stochastic computation represents a generative story about data.

Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling.

Pyro - Deep universal probabilistic programming with Python and PyTorch

Dialogflow is an enterprise-grade NLU platform that makes it easy for developers to design and integrate conversational user interfaces into mobile apps, web applications, devices, and bots. Select or create a Cloud Platform project.

Node.js client for Dialogflow - Design and integrate a conversational user interface into your applications and devices. 

NowTrade is an algorithmic trading library with a focus on creating powerful strategies using easily-readable and simple Python code. With the help of NowTrade, full blown stock/currency trading strategies, harnessing the power of machine learning, can be implemented with few lines of code. NowTrade strategies are not event driven like most other algorithmic trading libraries available. The strategies are implemented in a sequential manner (one line at a time) without worrying about events, callbacks, or object overloading.

NowTrade - Algorithmic trading library with a focus on creating powerful strategies

NFStream is a Python package providing fast, flexible, and expressive data structures designed to make working with online or offline network data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world network data analysis in Python. Additionally, it has the broader goal of becoming a common network data processing framework for researchers providing data reproducibility across experiments. NFStream extracts +90 flow features and can convert it directly to a pandas Dataframe or a CSV file.


NFStream - A Flexible Network Data Analysis Framework

Orange is a component-based data mining software. It includes a range of data visualization, exploration, preprocessing and modeling techniques. It supports . interactive data analysis workflows with a large toolbox. 

Orange - Data Mining Suite

Jubatus is a distributed processing framework and streaming machine learning library. Jubatus includes these functionalities: Online Machine Learning Library: Classification, Regression, Recommendation (Nearest Neighbor Search), Graph Mining, Anomaly Detection, Clustering, Feature Vector Converter (fv_converter): Data Preprocess and Feature Extraction, Framework for Distributed Online Machine Learning with Fault Tolerance.


Jubatus - Framework and Library for Distributed Online Machine Learning

MMLSpark provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.MMLSpark requires Scala 2.11, Spark 2.1+, and either Python 2.7 or Python 3.5+. See the API documentation for Scala and for PySpark.
 
Its features include : 
<ul>
<li>Easily ingest images from HDFS into Spark </li>
<li>Pre-process image data using transforms from OpenCV </li>
<li>Featurize images using pre-trained deep neural nets using CNTK </li>
<li> Use pre-trained bidirectional LSTMs from Keras for medical entity extraction</li>
<li>Train DNN-based image classification models on N-Series GPU VMs on Azure</li>
<li>Train classification and regression models easily via implicit featurization of data </li>
<li>Compute a rich set of evaluation metrics including per-instance metrics</li>
</ul>

MMLSpark provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.MMLSpark requires Scala 2.11, Spark 2.1+, and either Python 2.7 or Python 3.5+. See the API documentation for Scala and for PySpark.

MMLSpark - Microsoft Machine Learning for Apache Spark

Guesslang detects the programming language of a given source code. It supports more than 50 programming languages and detects the correct programming language with more than 90% accuracy. It is based on Tensorflow machine learning framework that have been trained with over a million source code files.





Guesslang - Detects the programming language of a given source code

Rubrix is a production-ready Python framework for exploring, annotating, and managing data in NLP projects.
Most annotation tools treat data collection as a one-off activity at the beginning of each project. In real-world projects, data collection is a key activity of the iterative process of ML model development. Once a model goes into production, you want to monitor and analyze its predictions, and collect more data to improve your model over time. Rubrix is designed to close this gap, enabling you to iterate as much as you need.

RUBRIX -  Python framework to explore, label, and monitor data for NLP

Dannjs is an easy-to-use neural network library for Javascript. Since this library is small and easy to learn, it acts as a playground for experimentation with deep neural networks. It is also fast to implement which makes it a great choice for various applications. 

Dannjs - Deep Neural Network Library for JavaScript

Chaos Genius is an open source ML powered analytics engine for outlier detection and root cause analysis. Chaos Genius can be used to monitor and analyse high dimensionality business, data and system metrics at scale. Using Chaos Genius, users can segment large datasets by key performance metrics (e.g. Daily Active Users, Cloud Costs, Failure Rates) and important dimensions (e.g., countryID, DeviceID, ProductID, DayofWeek) across which they want to monitor and analyse the key metrics.

Chaos Genius - ML powered analytics engine for outlier detection and root cause analysis

Apache Spot is a community-driven cybersecurity project, built from the ground up, to bring advanced analytics to all IT Telemetry data on an open, scalable platform. pot expedites threat detection, investigation, and remediation via machine learning and consolidates all enterprise security data into a comprehensive IT telemetry hub based on open data models.
 
Apache Spot uses the best tools just to provide the best functionality preventing attacks, identifying unknown threats, and providing cybersecurity. Perimeter Flows, DNS, Proxy and Internal Flows provide remarkable advantages creating fast and efficient data analysis.
 
 Apache Spot uses machine learning as a filter for separating bad traffic from benign and to characterize the unique behavior of network traffic. A proven process, of context enrichment, noise filtering, whitelisting and heuristics, is also applied to network data to produce a shortlist of most likely security threats.

Apache Spot is a community-driven cybersecurity project, built from the ground up, to bring advanced analytics to all IT Telemetry data on an open, scalable platform. pot expedites threat detection, investigation, and remediation via machine learning and consolidates all enterprise security data into a comprehensive IT telemetry hub based on open data models.

Apache Spot - A Community Approach to Fighting Cyber Threats

AI Tool for querying natural language on tabular data.Built using QA models from transformers.
 
Features: 
<ul>
<li>Supports detection from multiple csvs (csvs can also be read from Amazon s3)</li>
<li>Supports FuzzyString implementation. i.e, incomplete column values in query can be automatically detected and filled in the query.</li>
<li>Supports Databases - SQLite, Postgresql, MySQL, Amazon RDS (Postgresql, MySQL).</li>
<li>Open-Domain, No training required.</li>
<li>Add manual schema for customized experience</li>
<li>Auto-generate schemas in case schema not provided</li>
<li>Data visualisations.</li>
</ul>

AI Tool for querying natural language on tabular data.Built using QA models from transformers.

TableQA - AI Tool for querying natural language on tabular data. 

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