MLOps is mostly data engineering
I've been an MLOps Engineer for around 3 years now and I mostly agree with the article. There is a big overlap between the ML-specific tools that are popping up on the market and traditionnal Data Engineering tools, and I think people are not always realizing that:<p>* Prometheus/Grafana/TSDB/... can be used to setup a model monitoring platform since you're observing metrics whether they are from an ML service or a normal service.<p>* Any service deployment tool can be used to deploy ML models, since they are services.<p>* AirFlow/Dagster/... can be used to orchestrate model training, since training a model is basically a data engineering task.<p>With that said, I still believe that there is space for ML-specific tools to be created.<p>* Model Monitoring tools (ArizeAI is the only one I've used) can be  tailored to be easily usable by ML Engineers without requiring DE knowledge.<p>* Deploying models in production has some specifities: things like GPU support, adaptive batching, ... Those specifities can be implemented inside a model deployment tool.<p>* Training orchestration is the only domain where I think there's truly no need for new tools.