nyc_traffic_flask
emlearn
nyc_traffic_flask | emlearn | |
---|---|---|
2 | 5 | |
2 | 378 | |
- | 4.8% | |
7.4 | 9.2 | |
9 months ago | about 1 month ago | |
Python | Python | |
- | MIT License |
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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.
nyc_traffic_flask
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Where to incorporate MLops into my project?
I have an on/off data science project that uses ML to predict traffic. Lately, I've been wanting to practice more of the Software dev side of data science like DE and MLops. I'm wondering if there's any ways to potentially inject elements of MLops into said project, just for the sake of practice.
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I made a Flask app that predicts traffic in an NYC Street.
https://github.com/benduong2001/nyc_traffic_flask Lemme know if it works.
emlearn
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EleutherAI announces it has become a non-profit
> My big gripe, and for obvious reasons, is that we need to step away from cloud-based inference, and it doesn't seem like anyone's working on that.
I think there are steps being taken in this direction (check out [1] and [2] for interesting lightweight transpile / ad-hoc training projects) but there is a lack of centralized community for these constrained problems.
[1] https://github.com/emlearn/emlearn
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Simple and embedded friendly C code for Machine Learning inference algorithms
Examples: Gaussian Mixture Models (GMM) for anomaly detection or clustering Mahalanobis distance (EllipticEnvelope) for anomaly detection Decision trees and tree ensembles (Random Forest, ExtraTrees) Feed-forward Neural Networks (Multilayer Perceptron, MLP) for classification Gaussian Naive Bayes for classification
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[D] Drop your best open source Deep learning related Project
https://github.com/emlearn/emlearn is a ML inference engine for microcontrollers and embedded systems, allowing to deploy models to any platform with a C99 compiler. Has also been used for network traffic analysis as a Linux kernel module, and embedded in Android apps.
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Regression with the C64
The C64 has 64 kB of RAM. That is more than many contemporary microcontrollers. Using something like https://github.com/emlearn/emlearn allows to generate portable C code of ML models for such targets. Should be able to classify digits (MNIST) no problem on such hardware. Assuming there is a workable C compiler available.
Disclosure: Maintainer of emlearn project.
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Ask HN: What are some tools / libraries you built yourself?
I built emlearn, a Machine Learning inference engine for microcontrollers and embedded systems. It allows converting traditional ML models to simple and portable C99, following best practices in embedded software (no dynamic allocations etc). https://github.com/emlearn/emlearn
What are some alternatives?
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