FinGPT
metaflow
| FinGPT | metaflow | |
|---|---|---|
| 11 | 24 | |
| 11,650 | 7,644 | |
| 3.9% | 2.0% | |
| 9.5 | 9.2 | |
| 25 days ago | 6 days ago | |
| Jupyter Notebook | Python | |
| MIT License | 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.
FinGPT
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GPT-4, without specialized training, beat a GPT-3.5 class model that cost $10B
There is also the open source FinGPT, that is claimed to beat GPT4 in some benchmarks at a fine tuning cost of $17.25.
https://github.com/AI4Finance-Foundation/FinGPT
- FLaNK Stack 05 Feb 2024
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Ask HN: How to Get into Quantitative Trading?
https://github.com/AI4Finance-Foundation/FinGPT
[1] Zipline is a Pythonic algorithmic trading library
- FinGPT
- FLaNK Stack Weekly for 20 June 2023
- GPT but for the Finance Industry
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FinGPT: Open-Source Financial Large Language Models
Code: https://github.com/AI4Finance-Foundation/FinGPT
- Is FinGPT Coming?
metaflow
- FLaNK Stack 05 Feb 2024
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metaflow VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
- In Need of Guidance: Implementing MLOps in a Complex Organization as a Junior Data Engineer
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What are some open-source ML pipeline managers that are easy to use?
I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home
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Needs advice for choosing tools for my team. We use AWS.
1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling.
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Selfhosted chatGPT with local contente
even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf.
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[OC] Gender diversity in Tech companies
They had to figure out video compression that worked at the volume that they wanted to deliver. They had to build and maintain their own CDN to be able to have a always available and consistent viewing experience. Don’t even get me started on the resiliency tools like hystrix that they were kind enough to open source. I mean, they have their own fucking data science framework and they’re looking into using neural networks to downscale video.. Sound familiar? That’s cause that’s practically the same thing as Nvidia’s DLSS (which upscales instead of downscales).
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Model artifacts mess and how to deal with it?
Check out Metaflow by Netflix
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Going to Production with Github Actions, Metaflow and AWS SageMaker
Github Actions, Metaflow and AWS SageMaker are awesome technologies by themselves however they are seldom used together in the same sentence, even less so in the same Machine Learning project.
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Small to Reasonable Scale MLOps - An Approach to Effective and Scalable MLOps when you're not a Giant like Google
It's undeniable that leadership is instrumental in any company and project success, however I was intrigued with one of their ML tool choices that helped them reach their goal. I was so curious about this choice that I just had to learn more about it, so in this article will be talking about a sound strategy of effectively scaling your AI/ML undertaking and a tool that makes this possible - Metaflow.
What are some alternatives?
modelscope - ModelScope: bring the notion of Model-as-a-Service to life.
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
EasyCV - An all-in-one toolkit for computer vision
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
keep - The open-source alert management and AIOps platform
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
prompt-engineering - Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
kedro-great - The easiest way to integrate Kedro and Great Expectations
StanfordQuadruped
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
alpaca_eval - An automatic evaluator for instruction-following language models. Human-validated, high-quality, cheap, and fast.
dvc - 🦉 ML Experiments and Data Management with Git