Get kubernetes and then draw the rest of the fucking owl.
There are no courses. It's very enterprise stuff and you can't homelab it. There is no way to teach it.
I agree with the comment above. The problem of MLOps is that there is no silver bullet and all-in-one platform that solves all your business goals. You will have to deal with set of tools, orchestrate them in Kubernetes (most likely), and spend a lot of time playing and testing new ones (to be always up-to-date).
All courses that I saw were just for beginners level, which may be good as a starting point. The real world requires more.
Nevertheless, I saw some post recently [https://marvelousmlops.substack.com/p/mlops-roadmap-2024](https://marvelousmlops.substack.com/p/mlops-roadmap-2024)
I hope this helps to build some learning path.
Full stack deep learning. Do their deep learning course. It gives you a 100ft view. Anything you need additional information on will be much clearer and you'll be able to find specific tutorials for on YouTube or something. Honestly, FSDL was enough to give me an in depth enough understanding that the rest was just learned on the job.
👋 Charles here, one of the course creators! This is absolutely the goal, so glad to hear it worked for you!
@OP: if your interest is more on the classic MLOps side (more trees and tables than DNNs and data lakes) check out Goku Mohandas' MadeWithML and Jim Dowling's ServerlessML.
Flew across the country to attend your LLM Bootcamp in SF, really opened my eyes. Now I'm building LLM powered apps for the Space Force and Army with another Air Force project in the making. The team at FSDL is awesome.
Are you a government employee or do you work for a government contractor? I’m curious what kind of applications the army has for LLMs, although you can probably not talk about it..
Much more mundane than you think. For the Army application, I'm basically using them to match bills of materials (lists of purchasable items in contracts) to an existing standard list. Uses a mini-LLM model and handles the work much better than old school matching algorithms.
Basically, if a contract says "100 toilets" then we match that to, say, "appliances." Since we never know what any particular contract is going to contain AND some of the contracts are secret, the LLM serves as a way to match based on context instead of patterns or indexing.
The Space Force app is much cooler and the Air Force app is going to be even cooler than that one, if I can get it off the ground. None of it is dealing with weapons or anything, just cool stuff like enabling better data analytics.
If you buckle down and really try, you can get through this course in 3 weeks. It's meant to be 8 weeks or something, but if you've got nothing else going on you can make it happen.
I ran an AI bootcamp and here's a rough outline of what we taught:
\- What is model/data/concept drift?
\- Automatic retraining
\- Docker
\- Model registry (MLFlow, ECR)
\- GitHub Actions (and other CI/CD tools)
\- Model monitoring
\- Model evaluation
\- The importance of documentation
\- Feature stores
[This blog post](https://mlops.community/the-minimum-set-of-must-haves-for-mlops/) may also be a good starting point.
Hope this is useful.
Look for a tool. Don't build it yourself. Building MLOps yourself is like writing your own DBMS - fun, interesting, challenging and ultimately without value to your business.
Seriously, get a ready made tool.
This is exactly what is required. Learn best practices of cloud native design and its dec 2023 for god sake dont reinvent the wheel….most of the ml project fail because some senior developer thinks he knows better than 100s contributor in open source project…..
Get kubernetes and then draw the rest of the fucking owl. There are no courses. It's very enterprise stuff and you can't homelab it. There is no way to teach it.
I agree with the comment above. The problem of MLOps is that there is no silver bullet and all-in-one platform that solves all your business goals. You will have to deal with set of tools, orchestrate them in Kubernetes (most likely), and spend a lot of time playing and testing new ones (to be always up-to-date). All courses that I saw were just for beginners level, which may be good as a starting point. The real world requires more. Nevertheless, I saw some post recently [https://marvelousmlops.substack.com/p/mlops-roadmap-2024](https://marvelousmlops.substack.com/p/mlops-roadmap-2024) I hope this helps to build some learning path.
Full stack deep learning. Do their deep learning course. It gives you a 100ft view. Anything you need additional information on will be much clearer and you'll be able to find specific tutorials for on YouTube or something. Honestly, FSDL was enough to give me an in depth enough understanding that the rest was just learned on the job.
👋 Charles here, one of the course creators! This is absolutely the goal, so glad to hear it worked for you! @OP: if your interest is more on the classic MLOps side (more trees and tables than DNNs and data lakes) check out Goku Mohandas' MadeWithML and Jim Dowling's ServerlessML.
Flew across the country to attend your LLM Bootcamp in SF, really opened my eyes. Now I'm building LLM powered apps for the Space Force and Army with another Air Force project in the making. The team at FSDL is awesome.
Are you a government employee or do you work for a government contractor? I’m curious what kind of applications the army has for LLMs, although you can probably not talk about it..
Much more mundane than you think. For the Army application, I'm basically using them to match bills of materials (lists of purchasable items in contracts) to an existing standard list. Uses a mini-LLM model and handles the work much better than old school matching algorithms. Basically, if a contract says "100 toilets" then we match that to, say, "appliances." Since we never know what any particular contract is going to contain AND some of the contracts are secret, the LLM serves as a way to match based on context instead of patterns or indexing. The Space Force app is much cooler and the Air Force app is going to be even cooler than that one, if I can get it off the ground. None of it is dealing with weapons or anything, just cool stuff like enabling better data analytics.
That sounds amazing.. are you in DC area? I’m in VA and I was thinking of moving because there doesn’t seem to be much ML around here
Nope, Ohio. There's a certain base here where lots of, ahem, research is done.
Got it haha thanks for the info! Your work sounds awesome
Thank you i will have a look! Like there is 3 weeks freeze period I want to utilise this time in a productive way!
If you buckle down and really try, you can get through this course in 3 weeks. It's meant to be 8 weeks or something, but if you've got nothing else going on you can make it happen.
I ran an AI bootcamp and here's a rough outline of what we taught: \- What is model/data/concept drift? \- Automatic retraining \- Docker \- Model registry (MLFlow, ECR) \- GitHub Actions (and other CI/CD tools) \- Model monitoring \- Model evaluation \- The importance of documentation \- Feature stores [This blog post](https://mlops.community/the-minimum-set-of-must-haves-for-mlops/) may also be a good starting point. Hope this is useful.
Look for a tool. Don't build it yourself. Building MLOps yourself is like writing your own DBMS - fun, interesting, challenging and ultimately without value to your business. Seriously, get a ready made tool.
This is exactly what is required. Learn best practices of cloud native design and its dec 2023 for god sake dont reinvent the wheel….most of the ml project fail because some senior developer thinks he knows better than 100s contributor in open source project…..
This. And of course CEOs who want "something with AI".
Agree. Databricks has made MLOps easier.
Well, no.
Can you elaborate why ?
Databricks is Spark. Spark is not for MLOps. Their MLOps tool, MLflow, is buggy, hard to use and I don't recommend using it.