From business context:
Turn to an ML System as follows:
The data process in a bond scoring ML system
The training and inference pipeline for a bond risk model
I like the wrapping up section of the author:
The best advice I give to companies now is to be proactive about tooling portability. Upgrades to MLOps tooling are going to happen every 18 months to 3 years because there are too many stages of MLOps for one tool to be the jack of all trades and the best in breed practices are evolving. Being able to quickly adapt to the right ensemble of tools for your team and your organization will accelerate the adoption of ML in your organization, and ensure ML can provide convincing value to stakeholders.
Read it here: https://alexchung1.medium.com/case-study-of-mlops-in-a-hedge-fund-from-zero-to-30m-f524b05788ff
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