kitops
aqueduct
kitops | aqueduct | |
---|---|---|
6 | 2 | |
188 | 521 | |
84.6% | 0.0% | |
9.7 | 8.7 | |
4 days ago | 11 months ago | |
Go | Go | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
kitops
- The Docker build – Docker run workflow missing from AI/ML?
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KitOps Release v0.2–Introducing Dev Mode and the ability to chain ModelKits
Currently dev mode is only available on MacOS, although we plan to expand it to additional platforms and include more inference runtimes and utilities for models, data, and code. File an issue in our GitHub repository telling us what platform we should tackle next, or how to improve the Kit dev command in general - we love community feedback!
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Streamlining AI/ML Deployment with ModelKits: Innovations and Future Directions
Yesterday, Brad Micklea, Jozu CEO and KitOps maintainer, was a guest on the Partially Redacted podcast hosted by Sean Falconer. The 45-minute conversation (which you can listen to here) covered a lot of ground. Specifically, they discussed the current state of the KitOps project, where the project is headed, and some of our early ideas for productizing and releasing Jozu, which builds on top of KitOps.
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Introducing the New GitHub Action for using Kit CLI on MLOps pipelines
As we continue to innovate and improve KitOps, your feedback is invaluable to us. Whether you're encountering challenges, have suggestions for new features, or simply want to share your success stories, we're all ears. You can provide feedback on our GitHub repo or our Discord channel.
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Say hello to Kit–An open source solution to MLOps complexity
You can learn more about Kit here: https://kitops.ml, and support us by [giving Kit a star on GitHub]!(https://github.com/jozu-ai/kitops)
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The transitory nature of MLOps: Advocating for DevOps/MLOps coalescence
And checkout the source code here: https://github.com/jozu-ai/kitops
aqueduct
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Aqueduct: Take Data Science to Production
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/aqueduct
What are some alternatives?
distribution-spec - OCI Distribution Specification
llama2.go - LLaMA-2 in native Go
glide - 🐦 A open blazing-fast simple model gateway for rapid development of production GenAI apps
CortexTheseus - Cortex - AI on Blockchain, Official Golang implementation
sematic - An open-source ML pipeline development platform
fullnamematchscore-go - Generates a match score of two person names from 0-100, where 100 is the highest, on how closely two individual full names match. The scoring is based on a series of tests, algorithms, AI, and an ever-growing body of Machine Learning-based generated knowledge
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
deeplake - Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
ml-serverless-course - Learn to build serverless ML systems with only Python as a prerequisite
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]