For mission critical production usage, I think to use high performance systems/languages is pretty good starting point. With the assumptions that you update your model conservatively(not often), there are enough engineering resources to maintain and 'port' models from python, since most data scientists are trained with python. I think in this type of workload and context, it makes sense use Rust for deployment.
Another type of workload I think it is much more common. It is the experimental projects that data scientists are trying to discover does those have enough ROI to be part of the production system. Those projects and deployments are requiring a quick turn around on iteration cycle. I am not sure Rust or even Swift are good tools, when typical data scientists are not well versed in those. Not to mention, usually in this setting, they don't have a lot of engineering resources they can use. Python is still the go to option for this type of work.
I think the article has the right intention, speed up ML production. For the experimental work setting, I think we can have the cake and eat it too. Data scientists, still use python and generate production ready deployment service without help from engineers.
We create an open source python lib/platform called BentoML(www.github.com/bentoml/bentoml). BentoML makes it easy to serving and deploying ML models in the cloud, from ML model to production API endpoint with few lines of code. You can try it out at this Google Colab notebook(https://colab.research.google.com/github/bentoml/BentoML/blo...)
BentoML works with multiply ML framworks(Tensorflow/fastai/pytorch/etc) and could generate different distribution formats (docker/AWS Lambda/CLI/Spark UDF) for your serving need. We also support custom runtime backend. Feel free to ping me or ask questions in our slack channel. We are pretty active there.