concrete-ml
hands-on-train-and-deploy-ml
concrete-ml | hands-on-train-and-deploy-ml | |
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8 | 6 | |
817 | 670 | |
4.7% | - | |
9.6 | 5.3 | |
5 days ago | 7 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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concrete-ml
- Show HN: Logistic Regression Training on Encrypted Data with FHE
- Training ML Models on Encrypted Data with Homomorphic Encryption (FHE)
- FLaNK Stack Weekly 5 September 2023
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Concrete: A fully homomorphic encryption compiler
If you just want to dive right in, this example from Concrete ML's repository is very clear:
https://github.com/zama-ai/concrete-ml#a-simple-concrete-ml-...
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Instead of banning ChatGPT for its potential data theft, why don't we use advanced encryption techniques (for example, Homomorphic encryption) to secure our data?
As for ease of use, you should take a look at Concrete. It turns high level python code into FHE equivalents without developers having to know cryptography: https://github.com/zama-ai/concrete-ml
- Concrete ML: transform machine learning models into a homomorphic equivalent
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Zama Open-Sources Concrete ML v0.2 To Support Data Scientists Without Any Prior Cryptography Knowledge To Automatically Turn Classical Machine Learning (ML) Models Into Their FHE Equivalent
Github: https://github.com/zama-ai/concrete-ml
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[P] XGboost, sklearn and others running over encrypted data
Hello everyone! Following this post [numpy over encrypted numpy in fhe we are releasing a new lib that allows popular machine learning frameworks to run over encrypted data: https://github.com/zama-ai/concrete-ml
hands-on-train-and-deploy-ml
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Where to start
There are 3 courses that I usually recommend to folks looking to get into MLE/MLOps that already have a technical background. The first is a higher-level look at the MLOps processes, common challenges and solutions, and other important project considerations. It's one of Andrew Ng's courses from Deep Learning AI but you can audit it for free if you don't need the certificate: - Machine Learning in Production For a more hands-on, in-depth tutorial, I'd recommend this course from NYU (free on GitHub), including slides, scripts, full-code homework: - Machine Learning Systems And the title basically says it all, but this is also a really good one: - Hands-on Train and Deploy ML Pau Labarta, who made that last course, actually has a series of good (free) hands-on courses on GitHub. If you're interested in getting started with LLMs (since every company in the world seems to be clamoring for them right now), this course just came out from Pau and Paul Iusztin: - Hands-on LLMs For LLMs I also like this DLAI course (that includes Prompt Engineering too): - Generative AI with LLMs It can also be helpful to start learning how to use MLOps tools and platforms. I'll suggest Comet because I work there and am most familiar with it (and also because it's a great tool). Cloud and DevOps skills are also helpful. Make sure you're comfortable with git. Make sure you're learning how to actually deploy your projects. Good luck! :)
- FLaNK Stack Weekly 5 September 2023
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YouTube channel on AI, ML, NLP and Computer Vision
And a new (but very promising-looking), free GitHub course from Pau Labarta: - Hands-on Train and Deploy ML
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Help regarding DS career choices
For a higher-level, more conceptual overview, Andrew Ng always has great courses on DeepLearning.ai (and they're free to audit if you don't officially need the certificate): - Machine Learning for Production For a more hands-on, in-depth tutorial, I'd recommend this course from NYU (free on GitHub), including slides, scripts, full-code homework: - Machine Learning Systems And a new (but very promising-looking), free GitHub course from Pau Labarta (looks like he's still filming some of the lecture videos, but the rest of the course is all there): - Hands-on Train and Deploy ML
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Recommendation for MLOps resources
- Hands-on Train and Deploy ML
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How to get into MLOps?
This is also a pretty promising-looking new course that focuses on deployment and automation. It looks like some of the video lectures are still under construction (like I said it's super new), but the code and notebooks are all there.
What are some alternatives?
concrete-numpy - Concrete-Numpy: A library to turn programs into their homomorphic equivalent.
paxml - Pax is a Jax-based machine learning framework for training large scale models. Pax allows for advanced and fully configurable experimentation and parallelization, and has demonstrated industry leading model flop utilization rates.
yolov7-object-tracking - YOLOv7 Object Tracking Using PyTorch, OpenCV and Sort Tracking
MLSys-NYU-2022 - Slides, scripts and materials for the Machine Learning in Finance Course at NYU Tandon, 2022
puck - The visual editor for React
Youtube2Webpage - I learn much better from text than from videos
concrete - Concrete: TFHE Compiler that converts python programs into FHE equivalent
openaidemo - Demo of how access the OpenAI API using Java 17
privaxy - Privaxy is the next generation tracker and advertisement blocker. It blocks ads and trackers by MITMing HTTP(s) traffic.