Containerize ML workflows for reproducibility, reuse, and scalable training and inference on Google Cloud

Efficiently share, discover, and re-use ML features at scale while conducting reproducible ML experiments with Vertex AI Feature Store

Understand the nuances of model evaluation in both predictive and generative AI, recognizing its crucial role within the MLOps lifecycle.

Identify and apply appropriate evaluation metrics for different generative AI tasks.

Efficiently evaluate generative AI with Vertex AI's diverse evaluation services, including both computation-based and model-based methods.

Implement best practices for LLM evaluation, to ensure robust and reliable model deployment in production environments.

Explain the use cases that drive the adoption of ML Orchestration.

Describe how Vertex AI drives MLOps automation, reproducibility, and scaling.

Implement production-grade pipelines using Vertex AI’s no-code Template Gallery.

Build hybrid pipeline workflows with Kubeflow and pre-built GCP components.