The model benchmark is not your production benchmark

1. 1Evaluate your model using metrics beyond accuracy, such as latency and throughput
2. 2Test your model in a production-like environment to identify potential issues
3. 3Consider the computational resources and infrastructure required to deploy your model
4. 4Monitor and optimize your model's performance in production
5. 5Compare your model's performance to other models and baselines in production

Data scientists and machine learning engineers can benefit from understanding the difference between model benchmarks and production benchmarks to ensure successful model deployment

💡 Model benchmark performance is not the same as production performance

🚨 A model's benchmark performance doesn't guarantee production readiness! 🚨