Performance Evaluation
Explore the role of MLOps in evaluating AI product performance by managing model retraining, hyperparameter tuning, and continuous testing. Understand how continuous integration, deployment, and testing practices help maintain AI models, safeguard against data drift, and ensure reliable AI product functionality.
We'll cover the following...
Training and hyperparameter tuning
MLOps helps us with accentuating the importance of retraining and hyperparameter tuning our models to deliver performance. Without having a built-out AI/ML pipeline that validates, trains, and retrains regularly, we won’t have a great handle on our product’s performance. Our MLOps team will essentially be made up of data scientists and ML and DL engineers who will be tasked with making adjustments to the hyperparameters of our model builds, testing those models, and retraining them when needed. This will need to be done in conjunction with ...