Open Source Data Science Tools

Catbird Linux is a USB pluggable Live Linux operating system built for media creation, web scraping, and software coding. It is the daily driver you want for retrieving data, making videos or podcasts, and making software tools to automate the repetitive tasks. It is ready for work in Python, Lua, and Go languages, with numerous packages for web scraping or downloading data via API calls. Using Catbird Linux, it is possible to accomplish in depth stock market analysis, track weather trends, follow social media sentiment, or do other tasks in data science. The system is programmer friendly, ready for creating and running the tools you use to measure and understand your world. In addition to search and GPT tools, you have what you need to take notes, write reports or presentations, record and edit audio or video. Under the hood, the system is tuned to be fast and responsive on modest equipment, with a real time kernel and lightweight tiling / tabbing window manager.

This is a bot to finds the news you want to see. It can be made to find the news that interests you and reject everything else. View on one page the most interesting headlines from many websites.

Seldon Server is a machine learning platform and recommendation engine built on Kubernetes. Seldon reduces time-to-value so models can get to work faster. Scale with confidence and minimize risk through interpretable results and transparent model performance. Seldon Core focuses purely on deploying a wide range of ML models on Kubernetes, allowing complex runtime serving graphs to be managed in production. Seldon Core is a progression of the goals of the Seldon-Server project but also a more restricted focus to solving the final step in a machine learning project which is serving models in production. Seldon Server is a machine learning platform that helps your data science team deploy models into production. It provides an open-source data science stack that runs within a Kubernetes Cluster. You can use Seldon to deploy machine learning and deep learning models into production on-premise or in the cloud (e.g. GCP, AWS, Azure).

SynapseML (previously MMLSpark) is an open source library to simplify the creation of scalable machine learning pipelines. SynapseML builds on Apache Spark and SparkML to enable new kinds of machine learning, analytics, and model deployment workflows. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with the Open Neural Network Exchange (ONNX), LightGBM, The Cognitive Services, Vowpal Wabbit, and OpenCV. These tools enable powerful and highly-scalable predictive and analytical models for a variety of data sources. SynapseML also brings new networking capabilities to the Spark Ecosystem. With the HTTP on Spark project, users can embed any web service into their SparkML models. For production-grade deployment, the Spark Serving project enables high throughput, sub-millisecond latency web services, backed by your Spark cluster.