metaflow
kedro-great
metaflow | kedro-great | |
---|---|---|
1 | 1 | |
6 | 52 | |
- | - | |
8.2 | 0.0 | |
28 days ago | over 1 year ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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.
metaflow
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[D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit? -> MY OWN CONCLUSIONS
There are community Forks supporting Kubernetes and KFP. But they are not yet a part of the main framework and support is fluctuating. I think support should be available in the future.
kedro-great
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[D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit? -> MY OWN CONCLUSIONS
I expected Great Expectations library to be recommended, but nobody told anything. Instead, unit testing and/or smoke tests using pytest. And checking them with Jenkins. Anyway, if Kedro ends up being our project template, I'll keep an eye on the plugin with Great Expectations.
What are some alternatives?
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
great_expectations - Always know what to expect from your data.
feast - Feature Store for Machine Learning
Poetry - Python packaging and dependency management made easy
metaflow-on-kubernetes-docs - Documentation For Running Metaflow on Kubernetes
streamlit - Streamlit — A faster way to build and share data apps.
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution