From the course: MLOps Essentials: Model Development and Integration
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Benchmarking models
From the course: MLOps Essentials: Model Development and Integration
Benchmarking models
- [Instructor] A key activity in the machine learning life cycle is benchmarking of models. With respect to machine learning, benchmarking is the activity of comparing models and their versions against baselines and other competing models to understand how they perform against each other with respect to the stated project requirements and environments. Benchmarking happens after a model is found to pass its fitness test and is ready for integration. Benchmarking tests the model in environments that are closer to production, and test for both performance and operational metrics. What setup is required for a benchmarking environment? First, it needs hardware compute power, like CPU, memory and discs. It also needs an isolated software and network setup where the benchmarking process won't be impacted by other activities. Next, it needs a test hardness. Both the baseline and the new model need to be subjected to the…
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