A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. Stored as pure Python. All in a modern, AI-native editor.
- notebooks
- python
- data-science
- machine-learning
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A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. Stored as pure Python. All in a modern, AI-native editor.
Turns Data and AI algorithms into production-ready web applications in no time.
📚 Parameterize, execute, and analyze notebooks
AI Infra / AI Orchestration / AI Control Plane
High-performance time-series aggregation for PostgreSQL
Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
CLI task management & automation tool
Elyra extends JupyterLab with an AI centric approach.
Build data pipelines with SQL and Python, ingest data from different sources, add quality checks, and build end-to-end flows.
Python Streaming DataFrames for Kafka
MLBox is a powerful Automated Machine Learning python library.
visual data prep powered by python
Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.
Easy pipelines for pandas DataFrames.
Move fast from data science prototype to pipeline. Capture, analyze, and transform messy notebooks into data pipelines with just two lines of code.
An extensible Java framework for building event-driven applications that break up XML and non-XML data into chunks for data integration
BatchFlow helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflows even for datasets that do not fit into memory.
Multi-Paradigm Pipeline Implementation
Integrated pipeline for multiplexed image analysis
Realistic synthetic events for testing, demos, and pipelines — streamed live or generated in bulk
Build, test, deploy, iterate - Dev and prod tool for data science pipelines
Ordered-concurrently a library for concurrent processing with ordered output in Go. Process work concurrently and returns output in a channel in the order of input. It is useful in concurrently processing items in a queue, and get output in the order provided by the queue.
A coding exercise: let's convert dirty machine learning code into clean code using a Pipeline - which is the Pipe and Filter Design Pattern applied to Machine Learning.
GTFS Data Pipeline for TfNSW Bus Datasets