Short List Of ML-Ops Resources
There is an increasing 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 his 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.
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I haven’t used them personally, but people have given these resources good reviews.
Honestly, I want to release a couple of my projects outside of 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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