oomstore
aqueduct
Our great sponsors
| oomstore | aqueduct | |
|---|---|---|
| 1 | 2 | |
| 84 | 521 | |
| - | 1.0% | |
| 2.6 | 8.7 | |
| about 2 years 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.
oomstore
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Learning Go for Machine Learning
However, building ML infrastructure in Go is definitely a good idea. We at oom.ai is building oomstore - a ML feature store - in Go. Check out https://github.com/oom-ai/oomstore if you're interested, but it requires quite some Go familiarity to understand the code.
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?
beneath - Beneath is a serverless real-time data platform ⚡️
llama2.go - LLaMA-2 in native Go
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]
starwhale - an MLOps/LLMOps platform