BentoML
metaflow
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| BentoML | metaflow | |
|---|---|---|
| 16 | 1 | |
| 6,521 | 6 | |
| 2.7% | - | |
| 9.8 | 8.4 | |
| 2 days ago | 13 days ago | |
| Python | Python | |
| 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.
BentoML
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Who's hiring developer advocates? (December 2023)
Link to GitHub -->
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project ideas/advice for entry-level grad jobs?
there are a few tools you can use as "cheat mode" shortcuts to give you a leg up as you're getting started. here's one: https://github.com/bentoml/BentoML
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Two high schoolers trying to use Azure/GCP/AWS- need help!
Then you can look into bentoml https://github.com/bentoml/BentoML which is used to deploy ml stuff with many more benifits.
- Ask HN: Who is hiring? (November 2022)
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[D] How to get the fastest PyTorch inference and what is the "best" model serving framework?
For 2), I am aware of a few options. Triton inference server is an obvious one as is the ‘transformer-deploy’ version from LDS. My only reservation here is that they require the model compilation or are architecture specific. I am aware of others like Bento, Ray serving and TorchServe. Ideally I would have something that allows any (PyTorch model) to be used without the extra compilation effort (or at least optionally) and has some convenience things like ease of use, easy to deploy, easy to host multiple models and can perform some dynamic batching. Anyway, I am really interested to hear people's experience here as I know there are now quite a few options! Any help is appreciated! Disclaimer - I have no affiliation or are connected in any way with the libraries or companies listed here. These are just the ones I know of. Thanks in advance.
- PostgresML is 8-40x faster than Python HTTP microservices
- Congratulations on v1.0, BentoML 🍱 ! You are r/mlops OSS of the month!
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Show HN: Truss – serve any ML model, anywhere, without boilerplate code
In this category I’m a big fan of https://github.com/bentoml/BentoML
What I like about it is their idiomatic developer experience. It reminds me of other Pythonic frameworks like Flask and Django in a good way.
I have no affiliation with them whatsoever, just an admirer.
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[P] Introducing BentoML 1.0 - A faster way to ship your models to production
Github Page: https://github.com/bentoml/BentoML
- Show HN: BentoML goes 1.0 – A faster way to ship your models to production
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.
What are some alternatives?
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
feast - Feature Store for Machine Learning
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
metaflow-on-kubernetes-docs - Documentation For Running Metaflow on Kubernetes
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
kedro-great - The easiest way to integrate Kedro and Great Expectations
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
great_expectations - Always know what to expect from your data.
kubeflow - Machine Learning Toolkit for Kubernetes