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

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

Kaskada is a unified event processing engine that provides all the power of stateful stream processing in a high-level, declarative query language designed specifically for reasoning about events in bulk and in real time.
Kaskada's query language builds on the best features of SQL to provide a more expressive way to compute over events. Queries are simple and declarative. Unlike SQL, they are also concise, composable, and designed for processing events. By focusing on the event-processing use case, Kaskada's query language makes it easier to reason about when things happen, state at specific points in time, and how results change over time.
Kaskada can be best used for Machine learning, Analytics, Monitoring, Reactive applications.
Features
<ul dir="auto">
<li>Stateful aggregations: Aggregate events to produce a continuous timeline whose value can be observed at arbitrary points in time.</li>
<li>Automatic joins: Every expression is associated with an &ldquo;entity&rdquo;, allowing tables and expressions to be automatically joined. Entities eliminate redundant boilerplate code.</li>
<li>Event-based windowing: Collect events as you move through time, and aggregate them with respect to other events. Ordered aggregation makes it easy to describe temporal interactions.</li>
<li>Pipelined operations: Pipe syntax allows multiple operations to be chained together. Write your operations in the same order you think about them. It's timelines all the way down, making it easy to aggregate the results of aggregations.</li>
<li>Row generators: Pivot from events to time-series. Unlike grouped aggregates, generators produce rows even when there's no input, allowing you to react when something doesn't happen.</li>
<li>Continuous expressions: Observe the value of aggregations at arbitrary points in time. Timelines are either &ldquo;discrete&rdquo; (instantaneous values or events) or &ldquo;continuous&rdquo; (values produced by a stateful aggregations). Continuous timelines let you combine aggregates computed from different event sources.</li>
<li>Native time travel: Shift values forward (but not backward) in time, allowing you to combine different temporal contexts without the risk of temporal leakage. Shifted values make it easy to compare a value &ldquo;now&rdquo; to a value from the past.</li>
<li>Simple, composable syntax: It is functions all the way down. No global state, no dependencies to manage, and no spooky action at a distance. Quickly understand what a query is doing, and painlessly refactor to make it DRY.</li>
</ul>

Kaskada is a unified event processing engine that provides all the power of stateful stream processing in a high-level, declarative query language designed specifically for reasoning about events in bulk and in real time.

Kaskada - Modern, open-source event-processing

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Orange - Data Mining Suite

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Kaskada - Modern, open-source event-processing

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Projects

Orange - Data Mining Suite

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

Kaskada - Modern, open-source event-processing

TechStack

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