Knowledge check

Completed

1.

A data scientist trains and logs a model with MLflow. When the data scientist deploys the model, the schema of the model's input and output isn't correct. What should the data scientist customize to fix the issue?

Customize the model's environment.

Change the model's flavor.

Customize the model's signature.

2.

A data scientist trained a deep learning model with TensorFlow. The deployed model is compute-intensive and needs to use the most optimal inference server for similar workloads. Which model type is compatible with compute-intensive and no-code deployments?

MLflow

Triton

Custom

You must answer all questions before checking your work.