MLSys-NYU-2022
you-dont-need-a-bigger-boat
MLSys-NYU-2022 | you-dont-need-a-bigger-boat | |
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9 | 5 | |
238 | 825 | |
- | - | |
10.0 | 3.0 | |
over 1 year ago | 12 months ago | |
Jupyter Notebook | Python | |
MIT License | MIT License |
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MLSys-NYU-2022
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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! :)
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background in ML, how can I get into DS career as a mid 40's guy with a family?
- Machine Learning Systems And a new (but very promising-looking), free GitHub course from Pau Labarta:
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YouTube channel on AI, ML, NLP and Computer Vision
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
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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
- Machine Learning Systems
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[Advice] MLOps Course recommendations
MLSys 2022 is an online course with slides, homework and full coding examples at https://github.com/jacopotagliabue/MLSys-NYU-2022/tree/main .The second part is entirely on MLOps with Comet, Metaflow, etc.
- MLSys-NYU-2022: NEW Other Models - star count:100.0
you-dont-need-a-bigger-boat
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[Advice] MLOps Course recommendations
And another really popular repo: https://github.com/jacopotagliabue/you-dont-need-a-bigger-boat
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MLOps level 4 tooling
we followed this blueprint - https://github.com/jacopotagliabue/you-dont-need-a-bigger-boat and ended up simplifying much of the stack to just metaflow, great expectations and snowflake
- UT Austin PGP-AIML vs OMSCS for transition from SWE to MLE
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Small to Reasonable Scale MLOps - An Approach to Effective and Scalable MLOps when you're not a Giant like Google
However, if you want a more realistic problem, more worked-out open source examples are found here, and here, and hopefully you will believe me that you don't need to be the size of Google to be able to tackle these types of Data Science problems.
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Tooling for various stages of production ML pipeline? data -> experimentation -> versioning -> deployment?
You don't need a bigger boat - The repo shows how several (mostly open-source) tools can be effectively combined together to run data pipelines at scale with very small teams. The project now features: https://github.com/jacopotagliabue/you-dont-need-a-bigger-boat
What are some alternatives?
hands-on-train-and-deploy-ml - Train and Deploy an ML REST API to predict crypto prices, in 10 steps
comet-examples - Examples of Machine Learning code using Comet.ml
demo-fraud-detection-with-p2p - Exploring Neo4j and Graph Data Science for Fraud Detection
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
post-modern-stack - Joining the modern data stack with the modern ML stack
starter-workflows - Accelerating new GitHub Actions workflows
hands-on-llms - 🦖 𝗟𝗲𝗮𝗿𝗻 about 𝗟𝗟𝗠𝘀, 𝗟𝗟𝗠𝗢𝗽𝘀, and 𝘃𝗲𝗰𝘁𝗼𝗿 𝗗𝗕𝘀 for free by designing, training, and deploying a real-time financial advisor LLM system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 𝘷𝘪𝘥𝘦𝘰 & 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴
mlops-zoomcamp - Free MLOps course from DataTalks.Club
metaflow-f1-predictor
mlcomops