Discover how to train custom models using AutoML, evaluate their accuracy, & deploy them with Vertex AI + integration with GCP & utilization of KFP SDK.

This session dives deep into constructing an end-to-end AutoML workflow using Vertex Pipelines. We'll explore the use of AutoML Tabular to train models with structured data, using the Dry beans dataset as an eg, a classification task predicting bean types based on characteristics.

While prebuilt Google Cloud Pipeline Components play a significant role in the pipeline, I'll also introduce a Python function-based custom component for model evaluation & metrics visualization. Will also showcase the versatility of Vertex Pipelines in simplifying complex ML workflows.

What Will You Learn:
- Learn how to construct end-to-end ML workflows with a focus on AutoML training.
- Explore the integration of AutoML Tabular with real-world datasets.
- Unlock the potential of custom components for advanced model evaluation and visualization.