You work for a bank. You have been asked to develop an ML model that will support loan application decisions. You need to determine which Vertex AI services to include in the workflow. You want to track the model’s training parameters and the metrics per training epoch. You plan to compare the performance of each version of the model to determine the best model based on your chosen metrics. Which Vertex AI services should you use?
To accurately track the model’s training parameters, metrics per training epoch, and compare the performance of each model version, you should use Vertex ML Metadata, Vertex AI Experiments, and Vertex AI TensorBoard. Vertex ML Metadata helps in tracking and storing all training parameters and metrics. Vertex AI Experiments organizes and manages the training runs, making it easier to compare different versions of the model. Vertex AI TensorBoard provides detailed visualizations of training metrics and model performance, enabling in-depth analysis of the model behavior at each training epoch.
Vertex ML Metadata: Tracks model training parameters, hyperparameters, metrics, and lineage information. Stores metadata in a central repository for easy access and comparison. Integrates seamlessly with Vertex AI Experiments and TensorBoard. Vertex AI Experiments: Organizes and manages model training runs as experiments. Visualizes experiment results, including metrics and parameter comparisons. Facilitates tracking of the best performing model versions. Vertex AI TensorBoard: Provides detailed visualizations of training metrics and model performance. Enables analysis of model behavior at each training epoch. Integrates with Vertex AI Experiments for seamless access to experiment data.
use Tensorboard to track the model’s training parameters and the metrics per training epoch.
Why not B ?
I went C
agree with pikachu007
Agree with C