awesome-self-supervised-speech-representation-learning
awesome-production-machine-learning
awesome-self-supervised-speech-representation-learning | awesome-production-machine-learning | |
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1 | 9 | |
4 | 16,178 | |
- | 2.3% | |
0.0 | 7.5 | |
almost 3 years ago | 3 days ago | |
- | MIT License |
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awesome-self-supervised-speech-representation-learning
awesome-production-machine-learning
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
One trove of treasures is the awesome-production-machine-learning repository on GitHub. This curated list provides a multitude of frameworks, libraries, and software designed to facilitate various stages of the ML lifecycle.
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
There is a cool, gigantic list for MLOps that I can recommend: https://github.com/EthicalML/awesome-production-machine-learning
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How much of a full DS project pipeline can I do for free?
There are a lot of frameworks and specific tools out there that try to make production ML projects viable; from specific like Airflow (orchestrating jobs) and MLflow (experiment tracking) to more complex ones like Kubeflow. You can have a grasp here.
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Sqldiff: SQLite Database Difference Utility
https://github.com/EthicalML/awesome-production-machine-lear...
- [D] What are the best resources to crack M L system design interviews?
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I'm looking for a tool that let's you visualize the models architecture like this. Any idea what it is called?
https://github.com/EthicalML/awesome-production-machine-learning I think you will find most of the tools to visualize the model on this link.
- Awesome production machine learning - curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning [free] [website] [@all]
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Crucial differences in MLOps for deep learning
2/ https://github.com/EthicalML/awesome-production-machine-learning
What are some alternatives?
Awesome-VAEs - A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
shap - A game theoretic approach to explain the output of any machine learning model.
Awesome-pytorch-list - A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
awesome-jax - JAX - A curated list of resources https://github.com/google/jax
netron - Visualizer for neural network, deep learning and machine learning models
awesome-mlops - :sunglasses: A curated list of awesome MLOps tools
awesome-ml-for-cybersecurity - :octocat: Machine Learning for Cyber Security
datascience - Curated list of Python resources for data science.
awesome-ocr
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Awesome-Federated-Learning - FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
awesome-db-tools - Everything that makes working with databases easier