android-bootstrap
metaflow
android-bootstrap | metaflow | |
---|---|---|
21 | 24 | |
60 | 7,630 | |
- | 1.8% | |
1.8 | 9.2 | |
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
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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.
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When is Lobe Image Classifying coming
lobe.ai says object detection is coming soon
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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% :-( :-(
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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?
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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:
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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.
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can't deploy lobe ai web
I can run the lobe.ai web version locally.
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Anyone have examples of great website copy for a SAAS product?
lobe.ai
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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
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[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 :)
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?
streamlit - Streamlit — A faster way to build and share data apps.
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
awesome-teachable-machine - Useful resources for creating projects with Teachable Machine models + curated list of already built Awesome Apps!
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
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]
cld3-kotlin - Bindings to Google's Compact Language Detector 3 to JVM Based Languages
kedro-great - The easiest way to integrate Kedro and Great Expectations
metaflow - Build and manage real-life data science projects with ease.
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
great_expectations - Always know what to expect from your data.
dvc - 🦉 ML Experiments and Data Management with Git