Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

This combination provides an integrated, serverless MLOps platform. Vertex AI Pipelines is Google Cloud's managed orchestration service designed for building and scheduling ML workflows, including model retraining. It natively supports executing steps within custom Docker containers. Vertex AI Prediction (formerly AI Platform Prediction) is a managed service specifically for deploying models for online prediction. It offers built-in autoscaling to handle fluctuating request loads and integrates with Cloud Monitoring to provide visibility into model performance, directly fulfilling all requirements of the scenario.

A. Vertex AI Pipelines and App Engine: App Engine is a general-purpose platform for applications, not a specialized, optimized service for ML model serving like Vertex AI Prediction.
C. Cloud Composer, BigQuery ML, and AI Platform Prediction: BigQuery ML does not support training with custom Docker containers; it is designed for training models on data within BigQuery using SQL syntax.
D. Cloud Composer, AI Platform Training with custom containers, and App Engine: While Cloud Composer can schedule workflows, Vertex AI Pipelines is more tightly integrated with the ML ecosystem. App Engine is not the optimal choice for model serving.

1. Vertex AI Pipelines: "Vertex AI Pipelines helps you to automate, monitor, and govern your ML systems by orchestrating your ML workflow in a serverless manner... You can use the Google Cloud Pipeline Components SDK to build components that run your code in a Docker container."

Source: Google Cloud Documentation, "Introduction to Vertex AI Pipelines".

2. Vertex AI Prediction: "When you deploy a custom-trained model or an AutoML model to an endpoint on Vertex AI, you create a DeployedModel resource. The DeployedModel resource serves online predictions from your model... For online prediction, Vertex AI scales the number of prediction nodes to handle the traffic to your models."

Source: Google Cloud Documentation, "Deploy a model to an endpoint".

3. Scheduling with Vertex AI Pipelines: "You can schedule a pipeline to run on a recurring basis by using the PipelineJobSchedule service."

Source: Google Cloud Documentation, "Schedule pipeline runs".

4. BigQuery ML Limitations: "BigQuery ML lets you create and execute machine learning models in BigQuery by using standard SQL queries... It supports a specific set of model types." This contrasts with the requirement for custom Docker containers, which allow for arbitrary model architectures and training code.

Source: Google Cloud Documentation, "Introduction to BigQuery ML".