Google Cloud Vertex AI Platform
Description
Comprehensive understanding of Google Cloud's unified AI development platform for building, deploying, and managing generative AI applications.
Learning Objectives
Learners will master the Google Cloud Vertex AI platform including its components, services, and tools for developing generative AI solutions, understand how to deploy and manage AI models, and utilize Vertex AI Studio for prototyping and experimentation.
Topics (7)
Exploration of Model Garden's repository of first-party Google models, third-party models, and open-source models, including selection criteria and deployment options.
Creation of end-to-end ML workflows including data preprocessing, model training, evaluation, and deployment using Kubeflow Pipelines on Vertex AI.
Understanding of IAM controls, data encryption, VPC service controls, and compliance frameworks applicable to AI workloads on Vertex AI.
Understanding how Vertex AI integrates with BigQuery, Cloud Storage, Pub/Sub, and other Google Cloud services to create end-to-end AI applications.
Comprehensive introduction to Vertex AI platform, its role in the Google Cloud ecosystem, and how it unifies machine learning workflows from data preparation to model deployment.
Hands-on exploration of Vertex AI Studio's interface for prompt design, model testing, parameter tuning, and code generation for AI applications.
Understanding of model versioning, monitoring, A/B testing, and automated retraining capabilities within the Vertex AI ecosystem.