Amazing resources to start learning MLOps, one of the most exciting areas in machine learning engineering:

📘 Introducing MLOps

An excellent primer to MLOps and how to scale machine learning in the enterprise.

https://t.co/GCnbZZaQEI
🎓 Machine Learning Engineering for Production (MLOps) Specialization

A new specialization by https://t.co/mEjqoGrnTW on machine learning engineering for production (MLOPs).

https://t.co/MAaiRlRRE7
⚙️ MLOps Tooling Landscape

A great blog post by Chip Huyen summarizing all the latest technologies/tools used in MLOps.

https://t.co/hsDH8DVloH
🎓 MLOps Course by Goku Mohandas

A series of lessons teaching how to apply machine learning to build production-grade products.

https://t.co/RrV3GNNsLW
🐙 Awesome MLOps

A collection of links and resources for MLOps.

https://t.co/PL3gsmSqMf
🐙 Machine Learning Ops

A collection of resources on how to facilitate Machine Learning Ops with GitHub.

https://t.co/Qvm1llaJqm
👩🏼‍💻 MLOps Community

A place to have discussions about MLOps.

https://t.co/BiJaGLSNWH
These are just some of the high-quality resources I have come across.

MLOps is still in a maturing phase so it's a great time to learn about this stuff.

It might also be useful in the future if you are planning to become a machine learning engineer.

More from elvis

The past month I've been writing detailed notes for the first 15 lectures of Stanford's NLP with Deep Learning. Notes contain code, equations, practical tips, references, etc.

As I tidy the notes, I need to figure out how to best publish them. Here are the topics covered so far:


I know there are a lot of you interested in these from what I gathered 1 month ago. I want to make sure they are high quality before publishing, so I will spend some time working on that. Stay


Below is the course I've been auditing. My advice is you take it slow, there are some advanced concepts in the lectures. It took me 1 month (~3 hrs a day) to take rough notes for the first 15 lectures. Note that this is one semester of

I'm super excited about this project because my plan is to make the content more accessible so that a beginner can consume it more easily. It's tiring but I will keep at it because I know many of you will enjoy and find them useful. More announcements coming soon!

NLP is evolving so fast, so one idea with these notes is to create a live document that could be easily maintained by the community. Something like what we did before with NLP Overview: https://t.co/Y8Z1Svjn24

Let me know if you have any thoughts on this?

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