hands-on-llms
hands-on-train-and-deploy-ml
hands-on-llms | hands-on-train-and-deploy-ml | |
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1 | 6 | |
2,329 | 661 | |
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8.7 | 7.0 | |
about 1 month ago | about 2 months ago | |
Jupyter Notebook | Python | |
MIT License | MIT License |
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hands-on-llms
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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! :)
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?
MLSys-NYU-2022 - Slides, scripts and materials for the Machine Learning in Finance Course at NYU Tandon, 2022
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.
finetuned-qlora-falcon7b-medical - Finetuning of Falcon-7B LLM using QLoRA on Mental Health Conversational Dataset
AutoGPTQ - An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.
Youtube2Webpage - I learn much better from text than from videos
doc_chat_api - Create a production level scalable chat bot API to respond from the fed data
openaidemo - Demo of how access the OpenAI API using Java 17
LLM-Finetuning-Hub - Toolkit for fine-tuning, ablating and unit-testing open-source LLMs. [Moved to: https://github.com/georgian-io/LLM-Finetuning-Toolkit]
concrete-ml - Concrete ML: Privacy Preserving ML framework built on top of Concrete, with bindings to traditional ML frameworks.
Local-LLM-Langchain - Load local LLMs effortlessly in a Jupyter notebook for testing purposes alongside Langchain or other agents. Contains Oobagooga and KoboldAI versions of the langchain notebooks with examples.
puck - The visual editor for React