Git LFS and MLOps pay signal

A machine learning model can be a 4 gigabyte file, and regular Git was built for source code measured in kilobytes, not model checkpoints measured in billions of parameters. Git Large File Storage fixes that by keeping a tiny text pointer in the repository while the real file lives on a separate server. (git-lfs.com / docs.github.com) That pointer trick changes what every clone, fetch, and checkout has to move around. Instead of dragging a full model file through every branch and every developer laptop, Git only tracks a small reference file inside the main history. (git-lfs.com / docs.github.com) GitHub has a hard 100 megabyte file limit for normal Git pushes, and it recommends keeping repositories under 1 gigabyte, with 5 gigabytes described as a strong upper limit. A team that drops datasets and model binaries straight into the main repository can hit those limits fast and make basic Git operations slow. (docs.github.com / docs.github.com) That is why the recent Git Large File Storage primer spread: it is not a niche cleanup trick, it is a way to stop continuous integration and continuous delivery pipelines from hauling around heavyweight artifacts they do not need on every run. Google’s machine learning operations guidance treats automation across training, testing, and deployment as a core part of production machine learning. (x.com / docs.cloud.google.com) Machine learning operations means applying software operations discipline to machine learning systems. Amazon Web Services defines it as practices that automate and simplify machine learning workflows and deployments, which is a polite way of saying models need the same kind of release engineering that apps do. (aws.amazon.com / learn.microsoft.com) In a production team, the hard part is rarely training one model once. The hard part is versioning data, reproducing results, promoting approved models, and making sure a deployment on Friday can still be explained on Monday. (cloud.google.com / learn.microsoft.com) Git Large File Storage fits into that workflow as plumbing. GitHub recommends committing the local `.gitattributes` file into the repository so fresh clones and forks know which paths should be treated as large-file pointers instead of ordinary files. (docs.github.com) The pay chatter around machine learning operations engineers makes more sense once you see the job this way. Companies are not paying only for someone who can train a model; they are paying for someone who can make models reproducible, deployable, observable, and cheap enough to run every day. (aws.amazon.com / cloud.google.com) The European salary thread that circulated this week put experienced machine learning operations and infrastructure engineers around the €80,000 to €100,000 range at roughly five years of experience. Public salary sites vary by country and methodology, but they consistently show that machine learning operations sits closer to platform engineering pay than to entry-level data work. (x.com / levels.fyi / salary.run) Put those two posts together and the signal is simple: the boring parts of machine learning are turning into the valuable parts. A team that knows when to keep code in Git, when to push artifacts into Git Large File Storage, and how to wire that into deployment pipelines is doing the work companies are paying real money for. (git-lfs.com / docs.github.com / x.com / x.com)