Google Professional-Machine-Learning-Engineer Exam With Confidence Using Practice Dumps

 The best option for monitoring the model to determine when retraining is necessary is to schedule a weekly query in BigQuery to compute the success metric. This option has the following advantages:

It allows the model performance to be evaluated regularly, based on the actual outcome of the recommendations. By computing the success metric, which is the percentage of articles that are opened within two days and read for at least one minute, you can measure how well the model is achieving its objective and compare it with the acceptable baseline.

It leverages the scalability and efficiency of BigQuery, which is a serverless, fully managed, and highly scalable data warehouse that can run complex queries over petabytes of data in seconds. By using BigQuery, you can access and analyze all the information needed to compute the success metric, such as the newsletter publication date, the article opening date, and the user reading time, without worrying about the infrastructure or the cost.

It simplifies the model monitoring and retraining workflow, as the weekly query can be scheduled and executed automatically using BigQuery’s built-in scheduling feature. You can also set up alerts or notifications to inform you when the success metric falls below the acceptable baseline, and trigger the model retraining process accordingly.

The other options are less optimal for the following reasons:

Option A: Using Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days introduces additional complexity and overhead. This option requires setting up and managing a Vertex AI Model Monitoring service, which is a managed service that provides various tools and features for machine learning, such as training, tuning, serving, and monitoring. However, using Vertex AI Model Monitoring to detect skew of the input features may not reflect the actual performance of the model, as skew is the discrepancy between the distributions of the features in the training dataset and the serving data, which may not affect the outcome of the recommendations. Moreover, using a sample rate of 100% and a monitoring frequency of two days may incur unnecessary cost and latency, as it requires analyzing all the input features every two days, which may not be needed for the model monitoring.

Option B: Scheduling a cron job in Cloud Tasks to retrain the model every week before the newsletter is created introduces additional cost and risk. This option requires creating and running a cron job in Cloud Tasks, which is a fully managed service that allows you to schedule and execute tasks that are invoked by HTTP requests. However, using Cloud Tasks to retrain the model every week may not be optimal, as it may retrain the model more often than necessary, wasting compute resources and cost. Moreover, using Cloud Tasks to retrain the model before the newsletter is created may introduce risk, as it may deploy a new model version that has not been tested or validated, potentially affecting the quality of the recommendations.

Option D: Scheduling a daily Dataflow job in Cloud Composer to compute the success metric introduces additional complexity and cost. This option requires creating and running a Dataflow job in Cloud Composer, which is a fully managed service that runs Apache Airflow pipelines for workflow orchestration. Dataflow is a fully managed service that runs Apache Beam pipelines for data processing and transformation. However, using Dataflow and Cloud Composer to compute the success metric may not be necessary, as it may add more steps and overhead to the model monitoring process. Moreover, using Dataflow and Cloud Composer to compute the success metric daily may not be optimal, as it may compute the success metric more often than needed, consuming more compute resources and cost.

 Comparing the training and evaluation losses of the current run is a good way to check if the model is overfitting or underfitting. If the losses are similar, it means that the model is generalizing well and can be deployed to a Vertex AI endpoint. Vertex AI endpoint is a service that allows you to serve your ML models online and scale them automatically. By using a training/serving skew threshold model monitoring, you can detect if there is a significant difference between the data used for training and the data used for serving. This can indicate that the model is becoming stale or inaccurate over time. When the model monitoring threshold is triggered, it means that the model needs to be retrained with the latest data. By redeploying the pipeline, you can automate the retraining process and update the model with the new data. This way, you can minimize the cost of retraining and ensure that your model is always up-to-date and accurate.  References :

Vertex AI documentation

Vertex AI endpoint documentation

Model monitoring documentation

Preparing for Google Clo ud Certification: Machine Learning Engineer Professional Certificate

AI Platform Training is a service that allows you to run your machine learning experiments on Google Cloud using various features, model architectures, and hyperparameters.  You can use AI Platform Training to scale up your experiments, leverage distributed training, and access specialized hardware such as GPUs and TPUs 1 . Cloud Monitoring is a service that collects and analyzes metrics, logs, and traces from Google Cloud, AWS, and other sources.  You can use Cloud Monitoring to create dashboards, alerts, and reports based on your data 2 .  The Monitoring API is an interface that allows you to programmatically access and manipulate your monitoring data 3 .

By using AI Platform Training and Cloud Monitoring, you can track and report your experiments while minimizing manual effort.  You can write the accuracy metrics from your experiments to Cloud Monitoring usin g the AI Platform Training Python package 4 . You can then query the results using the Monitoring API and compare the performance of different experiments.  You can also visualize the metrics in the Cloud Console or create custom dashboards and alerts 5 . Therefore, using AI Platform Training and Cloud Monitoring is the best option for this use case.