feast
streamlit
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feast | streamlit | |
---|---|---|
8 | 255 | |
5,255 | 31,717 | |
1.9% | 3.6% | |
9.3 | 9.8 | |
4 days ago | about 12 hours 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.
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
streamlit
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Building an Email Assistant Application with Burr
Note that there are many tools that make this easier/simpler to prototype, including chainlit, streamlit, etc… The backend API we built is amenable to interacting with them as well.
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Creating a Sales Analysis Application with Streamlit: A Practical Approach to Business Intelligence
2.-Go to https://streamlit.io, log in, and create a new app from your GitHub repository.
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🦙 Llama-2-GGML-CSV-Chatbot 🤖
Developed using Langchain and Streamlit technologies for enhanced performance.
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Python dev considering Electron vs. Kivy for desktop app UI
Hello,
Have you ever seen the https://streamlit.io/ ? I think this is what you are looking for.
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Show HN: Buefy Web Components for Streamlit
While building dashboards in Streamlit, I found myself really missing Buefy's (Bulma) modern web components.
Specially due to the inability to add new values to Streamlit's multiselect [1], some missing controls like a polished image carousel [2] or a highly customizable data table.
Long story short, we put together streamfy (Streamlit + Buefy) as an MIT licensed project in GitHub to bring Buefy to Streamlit.
Demo: https://streamfy.streamlit.app
All the form components are implemented, missing half of other non-form UX components. There is plenty of room for PRs, testing, feedback, documentation, example, etc.
Please send issues and contributions to GitHub project [3] and general feedback to X / Twitter [4]
Thanks!
[1] https://github.com/streamlit/streamlit/issues/5348
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Simplify Web App Development: Code Lite, Create Big!
Here's your savior, let's welcome Streamlit.
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Show HN: Hyperdiv – Reactive, immediate-mode web UI framework for Python
Looks cool. How do you see this differing from streamlit? https://streamlit.io/
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Revolutionizing Real-Time Alerts with AI, NATs and Streamlit
Imagine you have an AI-powered personal alerting chat assistant that interacts using up-to-date data. Whether it's a big move in the stock market that affects your investments, any significant change on your shared SharePoint documents, or discounts on Amazon you were waiting for, the application is designed to keep you informed and alert you about any significant changes based on the criteria you set in advance using your natural language. In this post, we will learn how to build a full-stack event-driven weather alert chat application in Python using pretty cool tools: Streamlit, NATS, and OpenAI. The app can collect real-time weather information, understand your criteria for alerts using AI, and deliver these alerts to the user interface.
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Using LangServe to build REST APIs for LangChain Applications
In this tutorial, you'll construct a fully functional Streamlit application from the ground up. Streamlit lets you turn simple data scripts into web applications without traditional front-end tools. This application will be capable of downloading audio from any YouTube video, transcribing it using Deepgram, and then summarizing the content with the assistance of Mistral 7B, all streamlined through the capabilities of Langchain.
- Ask HN: Can I create a mobile and Web App using Python/Python Framework?
What are some alternatives?
kedro-great - The easiest way to integrate Kedro and Great Expectations
PyWebIO - Write interactive web app in script way.
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!
superset - Apache Superset is a Data Visualization and Data Exploration Platform
great_expectations - Always know what to expect from your data.
reflex - 🕸️ Web apps in pure Python 🐍
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.
PySimpleGUI - Python GUIs for Humans! PySimpleGUI is the top-rated Python application development environment. Launched in 2018 and actively developed, maintained, and supported in 2024. Transforms tkinter, Qt, WxPython, and Remi into a simple, intuitive, and fun experience for both hobbyists and expert users.