Short list of ML-Ops resources

There is increasing the need for software engineering practices to be applied to ML. This is the field of MLops.

This is a field I want to learn about. A redditor noticed that that the area seems a bit fragmented. And does not know what combinations would be suitable for use case.

I know very little about the topic. But I will provide some links I’ve seen, and I want to use. To learn about the subject.

Full-stack deep learning: A course that focuses on the production side of ML.

DVCorg ML-Ops tutorials: A YouTube playlist showing how to use GitHub actions for ml ops

Github actions for ML-ops: A blog post from GitHub showing how GitHub actions can be used for ML-ops and data science

MLOps Tooling Landscape v2 (+84 new tools) - Dec '20: A decent rundown of the ML-Ops field. (Also follow the author she writes regularly about ML-ops. Chip Huyen)

How to Deploy a Machine Learning Model to Google Cloud for 20% Software Engineers (CS329s tutorial): Example of deploying a model

GitHub for MLOps: Collection of blog posts using GitHub for ML-ops

How to improve software engineering skills as a researcher: A guide showing how to use software engineering tools for your deep learning model.

More Suggestions from Redditors:

Machine Learning Engineering for Production (MLOps) Specialization: Course by Andrew Ng about MLops

Gokul Mohandas Tutorial: MLOps course teaching the beginners about ML-ops

AI Engineering Youtube playlist: Youtube playlist about MLOps

DataTalks.Club DataOps: Podcast episode about DataOps

DataTalks.Club MLOps: Podcast episode about MLOPs

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I haven’t used them personally, but people have given these resources good reviews.

Honestly, I want to release couple of my projects outside a Google Colab notebook. Also learning about ML ops seems helpful for industry experience. (I don’t know, please correct me if I’m wrong)

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