GreenGuardian VS PulmoLens

Compare GreenGuardian vs PulmoLens and see what are their differences.

GreenGuardian

creating a greener globe with object detection models and batch transformation pipelines 🤖💚 (by akkik04)

PulmoLens

aws-powered deep-learning pneumonia detection model deployed as a serverless rest-api 🤖🫁 (by akkik04)
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GreenGuardian PulmoLens
6 3
3 10
- -
8.0 5.3
6 months ago almost 1 year ago
Jupyter Notebook Jupyter Notebook
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

GreenGuardian

Posts with mentions or reviews of GreenGuardian. We have used some of these posts to build our list of alternatives and similar projects.

PulmoLens

Posts with mentions or reviews of PulmoLens. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing GreenGuardian and PulmoLens you can also consider the following projects:

pytorch-serving-workshop - Slides and notebook for the workshop on serving bert models in production

video-super-resolution-youtube

amazon-sagemaker-examples - Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!

hugging-face-workshop - A 90-minute hands on workshop about Hugging Face on SageMaker.

mlops-course - Learn how to design, develop, deploy and iterate on production-grade ML applications.

fraud-detection-using-machine-learning - Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker

SagemakerHuggingfaceDashboard - This is a solution that demonstrates how to train and deploy a pre-trained Huggingface model on AWS SageMaker and publish an AWS QuickSight Dashboard that visualizes the model performance over the validation dataset and Exploratory Data Analysis for the pre-processed training dataset.