Google Professional Machine Learning Engineer Practice Test - Questions Answers, Page 2

Batch prediction is the process of using an ML model to make predictions on a large set of data points. Batch prediction is suitable for scenarios where the predictions are not time-sensitive and can be done in batches, such as digitizing scanned customer forms at the end of each day. Batch prediction can also handle large volumes of data and scale up or down the resources as needed. AI Platform provides a batch prediction service that allows users to submit a job with their TensorFlow model and input data stored in Cloud Storage, and receive the output predictions in Cloud Storage as well. This service requires minimal manual intervention and can be automated with Cloud Scheduler or Cloud Functions. Therefore, using the batch prediction functionality of AI Platform is the best option for this use case.

Batch prediction overview

Using batch prediction

TFX ModelValidatoris a tool that allows you to compare new models against a baseline model and evaluate their performance on different metrics and data slices1. You can use this tool to validate your models before deploying them to production and ensure that they meet your expectations and requirements.

k-fold cross-validationis a technique that splits the data into k subsets and trains the model on k-1 subsets while testing it on the remaining subset.This is repeated k times and the average performance is reported2. This technique is useful for estimating the generalization error of a model, but it does not account for the dynamic nature of customer behavior or the potential changes in data distribution over time.

Using the last relevant week of data as a validation setis a simple way to check the model's performance on recent data, but it may not be representative of the entire data or capture the long-term trends and patterns. It also does not allow you to compare the model with a baseline or evaluate it on different data slices.

Using the entire dataset and treating the AUC ROC as the main metricis not a good practice because it does not leave any data for validation or testing. It also assumes that the AUC ROC is the only metric that matters, which may not be true for your business problem. You may want to consider other metrics such as precision, recall, or revenue.

Labels are key-value pairs that can be attached to any AI Platform resource, such as jobs, models, versions, or endpoints1. Labels can help you organize your resources into descriptive categories, such as project, team, environment, or purpose.You can use labels to filter the results when you list or monitor your resources, or to group them for billing or quota purposes2. Using labels is a simple and scalable way to manage your AI Platform resources without creating unnecessary complexity or overhead. Therefore, using labels to organize resources is the best strategy for this use case.

Using labels

Filtering and grouping by labels

Oscillation in the loss during batch training of a neural network means that the model is overshooting the optimal point of the loss function and bouncing back and forth. This can prevent the model from converging to the minimum loss value. One of the main reasons for this phenomenon is that the learning rate hyperparameter, which controls the size of the steps that the model takes along the gradient, is too high. Therefore, decreasing the learning rate hyperparameter can help the model take smaller and more precise steps and avoid oscillation.This is a common technique to improve the stability and performance of neural network training12.

Interpreting Loss Curves

Is learning rate the only reason for training loss oscillation after few epochs?

L1 regularization, also known as Lasso regularization, adds the sum of the absolute values of the model's coefficients to the loss function1.It encourages sparsity in the model by shrinking some coefficients to precisely zero2. This way, L1 regularization can perform feature selection and remove the non-informative features from the model while keeping the informative ones in their original form. Therefore, using L1 regularization is the best technique for this use case.

Regularization in Machine Learning - GeeksforGeeks

Regularization in Machine Learning (with Code Examples) - Dataquest

L1 And L2 Regularization Explained & Practical How To Examples

L1 and L2 as Regularization for a Linear Model

Transfer learning is a technique that leverages the knowledge and weights of a pre-trained model and adapts them to a new task or domain1.Transfer learning can save time and resources by avoiding training a model from scratch, and can also improve the performance and generalization of the model by using a larger and more diverse dataset2.AI Platform provides several established text classification models that can be used for transfer learning, such as BERT, ALBERT, or XLNet3.These models are based on state-of-the-art natural language processing techniques and can handle various text classification tasks, such as sentiment analysis, topic classification, or spam detection4. By using one of these models on AI Platform, you can customize the model's code, serving, and deployment, and use Kubeflow pipelines for the ML platform. Therefore, using an established text classification model on AI Platform to perform transfer learning is the best option for this use case.

Transfer Learning - Machine Learning's Next Frontier

A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning

Text classification models

Text Classification with Pre-trained Models in TensorFlow

The best way to distribute the training examples across the train-test-eval subsets while maintaining the 80-10-10 proportion is to use option C. This option ensures that each subset contains a balanced and representative sample of the different classes (Democrat and Republican) and the different authors. This way, the model can learn from a diverse and comprehensive set of articles and avoid overfitting or underfitting. Option C also avoids the problem of data leakage, which occurs when the same author appears in more than one subset, potentially biasing the model and inflating its performance. Therefore, option C is the most suitable technique for this use case.

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 TPUs1. 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 data2.The Monitoring API is an interface that allows you to programmatically access and manipulate your monitoring data3.

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 using the AI Platform Training Python package4. 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 alerts5. Therefore, using AI Platform Training and Cloud Monitoring is the best option for this use case.

AI Platform Training documentation

Cloud Monitoring documentation

Monitoring API overview

Using Cloud Monitoring with AI Platform Training

Viewing evaluation metrics

Federated learning is a machine learning technique that enables organizations to train AI models on decentralized data without centralizing or sharing it1.It allows data privacy, continual learning, and better performance on end-user devices2.Federated learning works by sending the model parameters to the devices, where they are updated locally on the device's data, and then aggregating the updated parameters on a central server to form a global model3. This way, the data never leaves the device and the model can learn from a large and diverse dataset.

Federated learning is suitable for the use case of building an ML-based biometric authentication for the bank's mobile app that verifies a customer's identity based on their fingerprint. Fingerprints are considered highly sensitive personal information and cannot be downloaded and stored into the bank databases. By using federated learning, the bank can train and deploy an ML model that can recognize fingerprints without compromising the data privacy of the customers. The model can also adapt to the variations and changes in the fingerprints over time and improve its accuracy and reliability. Therefore, federated learning is the best learning strategy for this use case.