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Aqueduct - Taking data science to production | Product Hunt

 1 year ago
source link: https://www.producthunt.com/posts/aqueduct
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Aqueduct

Taking data science to production

Aqueduct automates the engineering required to take data science to production. By abstracting away low-level cloud infrastructure, Aqueduct enables data teams to run models anywhere, publish predictions where they're needed, and monitor results reliably.
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Hi everyone!

We've been working on making data teams more productive with Aqueduct for over a year, and we're really excited to share what we've been building.

There's a large (and growing!) number of programmers in the world who understand data and can solve business problems but don't want to spend their time wrangling low-level cloud infrastructure to get their work into the cloud. The existing MLOps tools that claim to solve this problem have been built by & for software teams, and they're incredibly complicated.

With Aqueduct, we've built a tool that's designed for data teams and abstracts away the underlying infrastructure. Aqueduct has a simple Python API that allows you to define a workflow as a composition of Python functions. Those workflows can be easily connected to data sources and can be run anywhere from your laptop to a Kubernetes cluster in the cloud. Once a workflow's running, Aqueduct has lightweight hooks to compute metrics and run tests over your pipelines to ensure they're producing high-quality results.

To learn more about what we're building, check out our GitHub repo or join our community Slack:

https://github.com/aqueducthq/aq... https://slack.aqueducthq.com

@vsreekanti Congrats, good luck 🚀 If you need some help with the logo and brand identity, feel free to contact me!
Andre here from the team. Happy to help answer any questions you may have!
Super excited to share! At Aqueduct, we're building an open-source platform that simplifies data teams' lives by raising the abstraction layer for production data science. We're working on some cool stuff under the hood: data caching, parallel operator scheduling, and compilation of high-level workflow definitions to low-level specs that run on powerful compute engines like Kubernetes and AWS Lambda. Let us know what you think!

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