Performance evaluation – testing, retraining, 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, you won’t have a great handle on your product’s performance. Your MLOps team will essentially be made up of data scientists and ML and DL engineers that will be tasked with making adjustments to the hyperparameters of your model builds, testing those models, and retraining them when needed. This will need to be done in conjunction with managing the data needed to feed this testing, along with the code base for your product’s interface as well.
In addition to testing and validating the models and working to clean and explore the data, MLOps team members also traditionally do software testing such as code tests, unit testing, and integration testing. In many cases, your AI...
