Made-With-ML
MLSys-NYU-2022
Made-With-ML | MLSys-NYU-2022 | |
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51 | 9 | |
35,702 | 238 | |
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6.8 | 10.0 | |
5 months ago | over 1 year ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | MIT License |
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Made-With-ML
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[D] How do you keep up to date on Machine Learning?
Made With ML
- Open-Source Production Machine Learning Course
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Advice for switching careers within analytics
- Develop a (simple!) ML project and apply MLOps best practices to it. Ask Chat GPT all of your MLOps questions. I've joined this MLOps community and it has been very helpful to know what path to follow in order to be better at MLOps, thanks to them I arrived at madewithml, but I haven't done it yet. But it covers all the MLOps side.
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Recommendation for MLOps resources
Hey, Iโm also working in ML. Hereโs a great resource: https://madewithml.com. Also, check out Noah Giftโs book Practical MLOPs.
- Ask HN: Resource to learn how to train and use ML Models
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Need help to find resources to learn ml ops
Try replicating this setup: https://madewithml.com/
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MLops Resources
madewithml
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Ask HN: How do I get started with MLOps?
There's a really nice website by Goku Mohandas called Made With ML. IMO it is the best practical guide to MLOps out there: https://madewithml.com
Incase you want to dive a little deeper, https://fullstackdeeplearning.com/course/2022/ is also something I have been recommended by folks.
- Resources for Current DE Interested in Learning Data Science
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Do organizations still need machine learning engineers?
madewithml is pretty sweet, especially the MLOps side of things. It'll give you good skills in how development in Python and deploying ML works.
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
What are some alternatives?
zero-to-mastery-ml - All course materials for the Zero to Mastery Machine Learning and Data Science course.
hands-on-train-and-deploy-ml - Train and Deploy an ML REST API to predict crypto prices, in 10 steps
mlops-zoomcamp - Free MLOps course from DataTalks.Club
you-dont-need-a-bigger-boat - An end-to-end implementation of intent prediction with Metaflow and other cool tools
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
demo-fraud-detection-with-p2p - Exploring Neo4j and Graph Data Science for Fraud Detection
mlops-course - Learn how to design, develop, deploy and iterate on production-grade ML applications.
post-modern-stack - Joining the modern data stack with the modern ML stack
practical-mlops-book - [Book-2021] Practical MLOps O'Reilly Book
hands-on-llms - ๐ฆ ๐๐ฒ๐ฎ๐ฟ๐ป about ๐๐๐ ๐, ๐๐๐ ๐ข๐ฝ๐, and ๐๐ฒ๐ฐ๐๐ผ๐ฟ ๐๐๐ for free by designing, training, and deploying a real-time financial advisor LLM system ~ ๐ด๐ฐ๐ถ๐ณ๐ค๐ฆ ๐ค๐ฐ๐ฅ๐ฆ + ๐ท๐ช๐ฅ๐ฆ๐ฐ & ๐ณ๐ฆ๐ข๐ฅ๐ช๐ฏ๐จ ๐ฎ๐ข๐ต๐ฆ๐ณ๐ช๐ข๐ญ๐ด
Copulas - A library to model multivariate data using copulas.
ETCI-2021-Competition-on-Flood-Detection - Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training