Sematic raises $3M Seed Round – Read the Announcement 🦊🚀

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ML Engineers

The ML tooling space is in its early days, join the Sematic community to be part of the ML revolution

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What our users are saying

"What I love about Sematic is that it's just Python. It's easy to implement with existing code, but also encourages better project design and thinking about the data being passed between steps.

It saves me a couple of hours every time I run the pipeline, which allows me to experiment more, and get better results."

– Blaine Bateman, Machine Learning Consultant

"Sematic gives us unparalleled visibility into our ML pipelines (artifacts, logs, errors, source control, dependency graph, etc.) while keeping the SDK and GUI simple and intuitive.

It provides just the right level of abstraction for ML engineers to focus on business logic and leverage cloud resources without requiring infrastructure skills.

Sematic is the kind of pipelining tool used by ML teams at Uber, now available to Voxel and everyone else."

– Anurag Kanungo, Co-founder, CTO of Voxel

"Sematic is simple but powerful. In a few minutes I was able to get a template pipeline running, and tailor it to my specific needs and view everything in a surprisingly informative, straightforward UI.

The level of debugability but scalability that Sematic provides is awesome; it is barely opinionated (just add a function decorator) but hugely flexible.

I could easily see it become a standard tool for Machine Learning orchestration, pipelining, and even scaling."

– Anton Bongio Karrman, Machine Learning Engineer, Ghost

Our latest videos

Sematic feature: Lineage Tracking and Git Integration

Sematic feature: Pipeline Observability with Exceptions and Logs in the UI

Sematic live coding – New Link type

Your first Sematic pipeline: MNIST in PyTorch

Sematic Beta Launch Demo

Sematic 101 – What is Sematic and why you need it

Why Sematic?

The easiest pipelining tool on the market

Just simple Python, no infrastructure
skills needed.

Traceability, observability, reproducibility

Get rich insights into inputs, outputs, logs, errors. Rerun pipelines from the UI with cached results.

Local-to-cloud parity

Run your pipelines on your local machine or in a GPU cluster with no
change in code.