MLOps for Risk Mitigation
• In machine learning systems there are risks associated with an organization.
• Proper risk assessment and mitigation are important to be done.
• MLOps is very important for risk mitigation for the ML models.
• The cost-benefit evaluations should be performed whenever an ML system is to be deployed.
Risk Assessment
• The stakes for different projects could be different in machine learning.
• Models that are deployed for wide usage and outside of the organization tend to pose larger risks.
• For risk mitigation using MLOps following things should be analyzed properly:
1. Unavailability of the model for a given time.
2. Bad predictions from the model
3. Decrement in model accuracy or fairness over time.
4. Loss of skills needed for maintaining the model.
• Risk should be quantized by analyzing the probability and the impact of each event.
• Here's a matrix that might help:
Risk Mitigation
• ML models should not be deployed without proper MLOps infrastructure in place.
• Only in a production environment, the full assessment of the model can be done as any change in production can rapidly decrease the model's performance.
• The versions of the frameworks used in building and deployment of the model are also very important so the deployment versions should match up with the versions the model was verified on.
• Pushing the models into production is not the end of the cycle but rather a beginning.
• As monitoring the models and using the feedback to improve or reduce problems is also a critical aspect of the production cycle.
Next time we will take a look at the people associated with the MLOps.
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