From the course: Introduction to NLP and LLMs: Principles and Practical Applications
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Metrics for model deployment and evaluation
From the course: Introduction to NLP and LLMs: Principles and Practical Applications
Metrics for model deployment and evaluation
- [Instructor] Let's explore the key metrics that guide successful NLP and LLM model deployment and evaluation projects. But first, why does it matter? Well, model performance is directly tied to business success. Effective models can enhance customer experiences, reduce costs, and improve overall efficiency. Monitoring performance helps identify opportunities for improvement and innovation. From a metrics perspective, model deployment should focus on operational performance and reliability. And model evaluation should focus on accuracy, business impact, and user satisfaction. Deployment is the first critical step in making your model available to users. At this stage, the focus is on ensuring that the model integrates seamlessly into the existing system and performs reliably under real-world conditions. Uptime measures the availability of the model or system. For example, a chatbot should have an uptime of 99.9%, ensuring minimal downtime for users. Response time tracks how quickly…
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