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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better hands-on-train-and-deploy-ml alternative or higher similarity.
hands-on-train-and-deploy-ml reviews and mentions
Posts with mentions or reviews of hands-on-train-and-deploy-ml.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-09-13.
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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.
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Stats
Basic hands-on-train-and-deploy-ml repo stats
6
658
7.0
about 2 months ago
Paulescu/hands-on-train-and-deploy-ml is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of hands-on-train-and-deploy-ml is Python.
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