dcai-lab
awesome-production-machine-learning
| dcai-lab | awesome-production-machine-learning | |
|---|---|---|
| 10 | 9 | |
| 399 | 16,430 | |
| 2.8% | 2.5% | |
| 5.4 | 7.5 | |
| 5 months ago | 5 days ago | |
| Jupyter Notebook | ||
| GNU Affero General Public License v3.0 | MIT License |
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dcai-lab
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Resources to learn practical/industry-focused ML (preferably using TensorFlow)?
Data-Centric AI honestly if you've been working on ML pipelines this might be familiar to you
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Andrew NG, github courses
Another great resource inspired by the Andrew Ng data-centric AI movement is the Introduction to Data-Centric AI course taught this past semester at MIT by PhDs.
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Good Beginner Courses for ML?
Data-centric AI course. Brand new, taught the 1st time a few months ago by MIT PhD grads. This covers how to ensure good data quality for your models. More data science havy.
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
Thanks for the kind words! Make sure to check out the current open MIT course if you are just starting out: https://dcai.csail.mit.edu/
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The Missing Semester of Your CS Education
Introduction to Data-Centric AI https://dcai.csail.mit.edu
- Introduction to Data-Centric AI
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MIT Introduction to Data-Centric AI
Course homepage | Lecture videos on YouTube | Lab Assignments
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?
snorkel - A system for quickly generating training data with weak supervision
shap - A game theoretic approach to explain the output of any machine learning model.
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
awesome-jax - JAX - A curated list of resources https://github.com/google/jax
BotLibre - An open platform for artificial intelligence, chat bots, virtual agents, social media automation, and live chat automation.
netron - Visualizer for neural network, deep learning and machine learning models
llm-course - Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
awesome-mlops - :sunglasses: A curated list of awesome MLOps tools
deodel - A mixed attributes predictive algorithm implemented in Python.
awesome-ml-for-cybersecurity - :octocat: Machine Learning for Cyber Security
chordviz - A convolutional neural network trained using PyTorch to predict the next chord (as tablature) on a guitar based on image data. Includes labeling software for the image data as well as an iOS app for hosting and running the model.
datascience - Curated list of Python resources for data science.