Passed Exam Today Professional-Machine-Learning-Engineer

A Dataflow streaming pipeline is a cost-effective way to process large volumes of real-time data from sensors. The RunInference API is a Dataflow transform that allows you to run online predictions on your streaming data using your ML models. By using the RunInference API, you can avoid the latency and cost of using a separate prediction service. The automatic model refresh feature enables you to update your models in the pipeline without redeploying the pipeline. This way, you can ensure that your models are always up-to-date and accurate. By deploying a Dataflow streaming pipeline with the RunInference API and using automatic model refresh, you can achieve sub-millisecond predictions, 24/7 availability, and low operational overhead for your ML models.  References :

Dataflow documentation

RunInference API documentation

Automatic model refresh documentation

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

Option A is correct because using F-score where recall is weighed more than precision is a suitable metric for binary classification with imbalanced data.  F-score is a harmonic mean of precision and recall, which are two metrics that measure the accuracy and completeness of the positive class 1 .  Precision is the fraction of true p ositives among all predicted positives, while recall is the fraction of true positives among all actual positives 1 . When the data is imbalanced, the positive class is the minority class, which is usually the class of interest. For example, in this case, the positive class is the images that contain the company’s logo, which are rare but important to detect.  By weighing recall more than precision, we can emphasize the importance of finding all the positive examples, even if some false positives are included 2 .

Option B is incorrect because using RMSE (root mean squared error) is not a valid metric for binary classification with imbalanced data.  RMSE is a metric that measures the average magnitude of the errors between the predicted and actual values 3 .  RMSE is suitable for regression problems, where the target variable is continuous, not for classification problems, where the target variable is discrete 4 .

Option C is incorrect because using F1 score is not the best metric for binary classification with imbalanced data.  F1 score is a special case of F-score where precision and recall are equally weighted 1 .  F1 score is suitable for balanced data, where the positive and negative classes are equally important and frequent 5 .  However, for imbalanced data, the positive class is more important and less frequent than the negative class, so F1 sc ore may not reflect the performance of the model well 2 .

Option D is incorrect because using F-score where precision is weighed more than recall is not a good metric for binary classification with imbalanced data.  By weighing precision more than recall, we can emphasize the impo rtance of minimizing the false positives, even if some true positives are missed 2 .  However, for imbalanced data, the true positives are more important and less frequent than the false positives, so this metric may not reflect the performance of the model well 2 .

This is not a good result because the model is performing worse than predicting that people will always renew their subscription. This option has the following reasons:

It indicates that the model is not learning from the data, but rather memorizing the majority class. Since 90% of the individuals renew their subscription every year, the model can achieve a 90% accuracy by simply predicting that everyone will renew their subscription, without considering the features or the patterns in the data. However, the model’s accuracy for predicting those who renew their subscription is only 82%, which is lower than the baseline accuracy of 90%. This suggests that the model is overfitting to the minority class (those who cancel their subscription), and underfitting to the majority class (those who renew their subscription).

It implies that the model is not useful for the business problem, as it cannot identify the customers who are at risk of churning. The goal of predicting whether customers will cancel their annual subscription is to prevent customer churn and increase customer retention. However, the model’s accuracy for predicting those who cancel their subscription is 99%, which is too high and unrealistic, as it means that the model can almost perfectly identify the customers who will churn, without any false positives or false negatives. This may indicate that the model is cheating or exploiting some leakage in the data, such as a feature that reveals the outcome of the prediction. Moreover, the model’s accuracy for predicting those who renew their subscription is 82%, which is too low and unreliable, as it means that the model can miss many customers who will churn, and falsely label them as renewing customers. This can lead to losing customers and revenue, and failing to take proactive actions to retain them.