Exam Professional Machine Learning Engineer topic 1 question 260 discussion

log_time_series_metrics requires setting Tensorboard: https://cloud.google.com/vertex-ai/docs/experiments/log-data assign_input_artifacts can be used to track input data: https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/get_started_with_vertex_experiments.ipynb

C sounds more correct

A and B are the same

Vertex AI Experiment and ML Metadata: This is the foundation for tracking experiments and artifacts within Vertex AI.expand_more Creating an experiment allows you to group related runs and log data associated with those runs. ML Metadata helps manage the lineage of data and models used in your experiments.expand_more Logging Data: log_time_series_metrics: This function is specifically designed for tracking time-series data, making it suitable for logging the preprocessed multivariate time series data in your experiment. log_metrics: This function is appropriate for logging loss values during model training. It can handle numerical values like loss efficiently. By combining these techniques, you can effectively track both the preprocessed data (time series) and the training performance metrics (loss values) within your Vertex AI Experiment.

Why B? 1. Experiment Creation: Vertex AI SDK establishes a context for grouping your training runs and facilitates experiment management. 2. By setting up Vertex ML Metadata (only can be done when creating an experiment with the Vertex AI SDK), you enable tracking of artifacts and metrics associated with each experiment run. 3. log_time_series_metrics function is well-suited for tracking the preprocessed multivariate time series data associated with each experiment run. This allows you to analyze how preprocessing impacts model performance.

B The assign_input_artifacts method is used to associate input artifacts with an experiment, that is not used for log time series and labels. A and B is just with a minor typo (metric vs merric), so select B.

My Answer: C assign_input_artifact method is a method to Vertex Ai Experiment to track the preprocessed data while log_time_series_metrics is a function of Vertex AI TensorBoard to log metrics along time. look: https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/build_model_experimentation_lineage_with_prebuild_code.ipynb https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/comparing_local_trained_models.ipynb

C. Tensorboard for experimentation and comparison of different model runs. assign_input_artifacts to track preprocessed data, since it links artifacts as inputs to the execution. https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.Execution#google_cloud_aiplatform_Execution_assign_input_artifacts Using log_time_series_metrics would make sense if what we were doing is logging a metric, which we aren't when we track the preprocessed data not yet ran by the model.