Google Updated Professional-Machine-Learning-Engineer Exam Questions and Answers by zidane

 XGBoost is a popular open-source library that provides a scalable and efficient implementation of gradient boosted trees. You can use XGBoost to train a classification or regression model on tabular data. You can also use Vertex AI to productionize the model and expose it for internal use as an HTTP microservice. Vertex AI is a service that allows you to create and train ML models using Google Cloud technologies. You can use a prebuilt XGBoost Vertex container to create a model and deploy it to Vertex AI Endpoints. A prebuilt Vertex container is a container image that contains the dependencies and libraries needed to run a specific ML framework, such as XGBoost. You can use a prebuilt Vertex container to simplify the model creation and deployment process, without having to build your own custom container. Vertex AI Endpoints is a service that allows you to serve your ML models online and scale them automatically. You can use Vertex AI Endpoints to deploy the model from the prebuilt Vertex container and expose it as an HTTP microservice. You can also configure the endpoint to handle a small number of incoming requests, and optimize the latency and cost of serving the model. By using a prebuilt XGBoost Vertex container and Vertex AI Endpoints, you can productionize the model with the least amount of effort and latency.  References :

XGBoost documentation

Vertex AI documentation

Prebuilt Vertex container documentation

Vertex AI Endpoints documentation

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

AI Platform supports hyperparameter tuning for PyTorch models using custom containers. This allows you to use any Python dependencies and libraries that are not included in the pre-built AI Platform Training runtime versions. You can also use a pre-trained model such as ResNet as a base for your custom model. To run a hyperparameter tuning job on AI Platform using custom containers, you need to do the following steps:

Create a Dockerfile that defines the container image for your training application. The Dockerfile should install PyTorch and any other dependencies, copy your training code and configuration files, and set the entrypoint for the container.

Build the container image and push it to Container Registry or another accessible registry.

Create a YAML file that defines the configuration for your hyperparameter tuning job. The YAML file should specify the container image URI, the training input and output paths, the hyperparameters to tune, the metric to optimize, and the tuning algorithm and budget.

Submit the hyperparameter tuning job to AI Platform using the gcloud command-line tool or the AI Platform Training API.

AutoML Vision Edge is a service that allows you to create custom image classification and object detection models that can run on edge devices, such as mobile phones, tablets, or IoT devices 1 .  AutoML Vision Edge offers four types of models that vary in size, accuracy, and latency: mobile-versatile-1, mobile-low-latency-1, mobile-hig h-accuracy-1, and mobile-core-ml-low-latency-1 2 . Each model has its own trade-offs and use cases, depending on the device specifications and the application requirements.

For the use case of building an ML model to reduce the amount of time spent by quality control inspectors checking for product defects, the best model to use is the AutoML Vision Edge mobile-low-latency-1 model.  This model is optimized for fast inference on mobile devices, with a latency of less than 50 milliseconds on a Pixel 1 phone 2 . Faster defect detection is a priority for the toy manufacturer, and the factory does not have reliable Wi-Fi, so a low-latency model that can run on the device without internet connection is ideal.  The mobile-low-latency-1 model also has a small size of less than 4 MB, which makes it easy to deploy and update 2 .  The mobile-low-latency-1 model has a slightly lower accuracy than the mobile-high-acc uracy-1 model, but it is still suitable for most image classification tasks 2 . Therefore, the AutoML Vision Edge mobile-low-latency-1 model is the best option for this use case.