wave
feast
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| wave | feast | |
|---|---|---|
| 21 | 8 | |
| 3,852 | 5,246 | |
| 1.0% | 1.7% | |
| 9.2 | 9.3 | |
| 11 days ago | 6 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.
wave
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Streamlit alternatives but for Rust?
https://streamlit.io/ https://wave.h2o.ai/ https://reflex.dev/
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Launch HN: Pynecone (YC W23) – Web Apps in Pure Python
Looks similar to Nitro https://nitro.h2o.ai/ and Wave https://wave.h2o.ai/ - both open source. Nitro already works with WebAssembly via Pyodide. (Author here)
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Nice GUI
To write web gui in Python, there are some other open source alternatives.
If just want to port simple shell interactive interface to web gui, can check https://github.com/pywebio/PyWebIO
If want to get a production level dashboard by using Python, https://wave.h2o.ai/ <>
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Show HN: Hstream – quick Python web apps (Streamlit alternative using Htmx)
I think the demo site may have been hugged to death. It's not rendering anything for me.
I think it's also worth giving a shoutout to wave in this space; they have quite a few components you can use out-of-the-box https://github.com/h2oai/wave
- PyScript
- Realtime Web Apps and Dashboards for Python
- Realtime Web Apps and Dashboards for Python and R
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[D] What do you use to build UIs for your projects?
H2O Wave : https://wave.h2o.ai
- Creating a web app in Python without knowledge of HTML/CSS/JavaScript
feast
- What's Happening with Feast?
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Running The Feast Feature Store With Dragonfly
Feast stands as an exceptional open-source feature store, revolutionizing the efficient management and uninterrupted serving of machine learning (ML) features for real-time applications. At its core, Feast offers a sophisticated interface for storing, discovering, and accessing features—the individual measurable properties or characteristics of data essential for ML modeling. Operating on a distributed architecture, Feast harmoniously integrates several pivotal components, including the Feast Registry, Stream Processor, Batch Materialization Engine, and Stores.
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Ask HN: How to Break into AI Engineering
AI Engineering is basically Data Engineering focused on AI. When in "traditional" Data Engineering you create pipelines that store processed data in something like a Data Lake, in AI Eng. your end storage might be a specialized Feature Storage (like Feast or GCP Vertex AI).
There are some AI Engineers with strong scientific/mathematical background, but that's rare. Usually, you're paired with these ML people that actually develop and evaluate the models.
So my advice is to start with Data Engineering and then find a specialization AI. You should have a VERY solid foundation on scripting and programming, specially Python. Also, a lot of concepts of "data wrangling". Understanding how data flows from point A to point B, how the intermediate storages and streaming engines work, etc. Functional programming is key here.
[0] https://github.com/feast-dev/feast
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In Need of Guidance: Implementing MLOps in a Complex Organization as a Junior Data Engineer
A feature store usually stores features which are used for training ML model. It is a centralized place for collaboration between data engineer, ML engineer, and data scientist, so that data engineer can write to the feature store while ML engineer and data scientist read from it. Hopsworks https://www.hopsworks.ai and feast https://github.com/feast-dev/feast are examples of open source feature store.
- [D] Your 🫵 Preferred Feature Stores?
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[P] Announcing Feast 0.10: The simplest way to serve features in production
Github: https://github.com/feast-dev/feast
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[D] What’s the simplest, most lightweight but complete and 100% open source MLOps toolkit? -> MY OWN CONCLUSIONS
Have you looked at Feats as a Feature Store solution? It seems promising but I haven't really looked into it yet though.
- Feast: OSS Feature Store for Production ML
What are some alternatives?
streamlit - Streamlit — A faster way to build and share data apps.
kedro-great - The easiest way to integrate Kedro and Great Expectations
reactpy - It's React, but in Python
featureform - The Virtual Feature Store. Turn your existing data infrastructure into a feature store.
gradio - Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
Milvus - A cloud-native vector database, storage for next generation AI applications
nicegui - Create web-based user interfaces with Python. The nice way.
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
pglet - Pglet - build internal web apps quickly in the language you already know!
great_expectations - Always know what to expect from your data.
dephell - :package: :fire: Python project management. Manage packages: convert between formats, lock, install, resolve, isolate, test, build graph, show outdated, audit. Manage venvs, build package, bump version.
mlrun - MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.