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Tangle
FreeOpen-source visual editor to build ML pipelines without coding, for data scientists.
Frustration: Building ML pipelines typically requires heavy coding and complex orchestration.
Tangle is an open-source visual editor that lets you build, manage, and execute pipelines with a drag-and-drop interface. It is platform-agnostic and can wrap existing code in containers using any language or framework. It also offers advanced execution caching to speed up experimentation and reduce compute costs.
The Aha Moment: Visual Pipelines, Faster Results
With Tangle, you design workflows by connecting components, containerize code, and run cached executions to iterate quickly, making ML experimentation accessible, reproducible, and cost-efficient.
Quick verdict
Based on 2 reviews
Pros
- Drag-and-drop interface lets me wire up an end-to-end pipeline—from data prep to training—without writing code.
- Execution caching lets me reuse results across experiments, cutting compute waste.
- Visual management makes it easy to monitor runs and tweak hyperparameters without touching the codebase.
Cons
- Wrapping legacy Python steps into containers requires extra boilerplate to fit our Kubernetes setup.
- The canvas slows a bit on bigger graphs, making drag-and-drop feel less responsive.
- Governance features like granular access controls and audit trails are missing, complicating collaboration at scale.
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Open-source visual editor to build ML pipelines without coding, for data scientists.
Customer Reviews for Tangle
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Most helpful
I can wrap Python, SQL, and Spark steps into containers and stitch them into a single visual workflow, which makes cross-language reuse effortless. Reproducible experiments across frameworks keep us aligned across data scientists. Visual run controls help me monitor, stop, and compare variants in real time. The initial wrapper setup for new languages is a bit fiddly, and we miss per-project access controls for multi-team onboarding.
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Showing 1 - 2 of 2 reviews .
Liam Miller
Trusted ReviewerI leverage platform-agnostic wrapping for cross-language workflows, with governance gaps to address
What I liked
What could be better
I can wrap Python, SQL, and Spark steps into containers and stitch them into a single visual workflow, which makes cross-language reuse effortless. Reproducible experiments across frameworks keep us aligned across data scientists. Visual run controls help me monitor, stop, and compare variants in real time. The initial wrapper setup for new languages is a bit fiddly, and we miss per-project access controls for multi-team onboarding.
Grace Walker
Trusted Reviewer Verified purchaseI built an end-to-end pipeline with drag-and-drop in under an hour, but wrapping needs polish
What I liked
What could be better
I've built an end-to-end ML flow in under an hour using the drag-and-drop editor, connecting data prep, feature extraction, and training with no code. The execution caching is a real win when I test variants. Yet wrapping legacy Python steps into containers requires extra boilerplate to fit our Kubernetes setup, and the canvas slows a bit on bigger graphs. Governance gaps also pop up when teams scale.
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How it works
How Tangle Works In 3 Steps?
1. Install Tangle
Download or run the open-source editor to start building ML pipelines visually.
2. Drag & Connect
Wire components to define data flow and model steps without coding.
3. Run & Iterate
Execute pipelines, view results, and cache experiments to save compute.
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Developed by: Shopify ML
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