android-bootstrap
feast
Our great sponsors
| android-bootstrap | feast | |
|---|---|---|
| 21 | 8 | |
| 60 | 5,255 | |
| - | 1.9% | |
| 1.8 | 9.3 | |
| about 3 years ago | 2 days ago | |
| Kotlin | Python | |
| GNU General Public License v3.0 or later | 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.
android-bootstrap
-
Build end-to-end AI Apps in minutes using just your phone.
This is interesting. The closest I can compare it to is lobe.ai.
-
When is Lobe Image Classifying coming
lobe.ai says object detection is coming soon
-
lobe.ai. new version
I need urgent help please!!! I've just installed the new Version of lobe.ai on my MAC and now, after it has finished, the prediction rate has decreased from more than 90% to 50% :-( :-(
-
Camera Works for "Label" But Not for "Use"
Using lobe.ai 0.10.1130.5 I successfully trained using my Webcam Logitech C920. The camera turned live, and I could take individual and rapid-snap photos. But after proceeding to 'Use', the camera button does show, but nothing happens when I press it, not does hovering raise a floating menu. What am I doing wrong?
-
Rasp Pi OS Bullseye has dropped support of PiCamera - breaks Lobe on Rasp P
Found a fix here? There is some bugs in the lobe.ai code:
-
Problem getting Lobe.io on Android Device with Android Studio
There is this android-bootstrap https://github.com/lobe/android-bootstrap. In that "getting started" I did everything but I feel like there are steps missing between 4 and 5. The "Run" button is grayed-out.
-
can't deploy lobe ai web
I can run the lobe.ai web version locally.
-
Anyone have examples of great website copy for a SAAS product?
lobe.ai
-
Android no metadata found for tflite model
Are you copying both the saved_model.tflite model and signature.json files? https://github.com/lobe/android-bootstrap#get-started
-
[UPDATE] My Isaac Item Recogniser app in action. Only Android version for now, beta test soon ;)
I have no plans on open sourcing my app, but here are bits and pieces I used to build upon :)
feast
- What's Happening with Feast?
-
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.
-
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
-
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?
-
[P] Announcing Feast 0.10: The simplest way to serve features in production
Github: https://github.com/feast-dev/feast
-
[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
awesome-teachable-machine - Useful resources for creating projects with Teachable Machine models + curated list of already built Awesome Apps!
featureform - The Virtual Feature Store. Turn your existing data infrastructure into a feature store.
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
Milvus - A cloud-native vector database, storage for next generation AI applications
cld3-kotlin - Bindings to Google's Compact Language Detector 3 to JVM Based Languages
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
metaflow - Build and manage real-life data science projects with ease.
great_expectations - Always know what to expect from your data.
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.