Data Science Model Deployments and Cloud Computing on GCP - Introduction-Vertex AI

This video provides an overview of Vertex AI, a managed machine learning platform on Google Cloud. It covers the entire machine learning lifecycle, including data preprocessing, model training, evaluation, and deployment. Vertex AI supports both UI and code-based deployments and integrates with Jupyter Notebooks. It supports major frameworks like TensorFlow and PyTorch. The video also covers interactive model development, containerization, and deployment using Python SDK and UI. It introduces Kubeflow Pipelines for orchestration and focuses on Scikit-learn for custom model deployments.

What is the primary purpose of Vertex AI on Google Cloud?

• To develop mobile applications

• To offer web hosting services

• To manage machine learning models throughout their lifecycle

• To provide cloud storage solutions

Which of the following frameworks is NOT supported by Vertex AI?

• Scikit-learn

• TensorFlow

• PyTorch

• Ruby on Rails

What tool is integrated with Vertex AI for interactive model development?

• Microsoft Excel

• Google Sheets

• Jupyter Notebooks

• Apache Spark

What is the role of Kubeflow Pipelines in Vertex AI?

• To manage cloud billing

• To create mobile applications

• To orchestrate machine learning workflows

• To provide data storage

Which framework is the focus of the practical steps section in Vertex AI?

• PyTorch

• Scikit-learn

• Keras

• TensorFlow