MLSys-NYU-2022 Alternatives

MLSys-NYU-2022 reviews and mentions

• Where to start
  3 projects | /r/mlops | 13 Sep 2023

  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! :)

• background in ML, how can I get into DS career as a mid 40's guy with a family?
  1 project | /r/datascience | 12 Jul 2023

  - Machine Learning Systems And a new (but very promising-looking), free GitHub course from Pau Labarta:

• YouTube channel on AI, ML, NLP and Computer Vision
  2 projects | /r/developersIndia | 9 Jul 2023

  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

• Help regarding DS career choices
  2 projects | /r/datascience | 26 Jun 2023

  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

• Recommendation for MLOps resources
  3 projects | /r/OMSCS | 25 Jun 2023

  - Machine Learning Systems

• [Advice] MLOps Course recommendations
  3 projects | /r/datascience | 24 Jun 2023

  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
  1 project | /r/algoprojects | 4 Feb 2023
  1 project | /r/algoprojects | 3 Feb 2023
  1 project | /r/algoprojects | 2 Feb 2023
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