The AI-based Fraud Detection System leverages Google Cloud's Vertex AI to deploy the most effective machine learning models for detecting fraudulent transactions in credit card data. This project encapsulates a workflow of training, evaluating, and deploying machine learning models in a cohesive pipeline, ensuring the deployment of the most accurate model available for fraud detection.
This Vertex AI Pipeline orchestrates the selection, deployment, and real-time monitoring of the highest-performing machine learning model from a pool of candidates. Designed to support a dynamic and collaborative model development environment, it ensures that only the most accurate and relevant models are deployed for fraud detection tasks.
The development and refinement of machine learning models for fraud detection is a continuous process that benefits from diverse approaches and collective expertise. This pipeline facilitates:
- Collaborative Development: Allows multiple team members to contribute models using varied techniques.
- Iterative Improvement: Supports ongoing enhancement of models based on performance evaluations.
- Unified Management: Models are systematically managed with a shared
serieslabel in the Vertex AI Model Registry, simplifying identification and comparison.
The pipeline is structured into key phases to streamline the model lifecycle management:
- Registry Query: Fetch models tagged with the
serieslabel and thedefaultversion alias from the Vertex AI Model Registry. - Performance Evaluation: Execute parallel assessments of candidates focusing on key metrics like accuracy, ROC curves, and confusion matrices.
- Model Selection: Identify the model that exhibits superior performance across the evaluation criteria.
- Endpoint Management: Evaluate the existing deployment state; if no model is actively deployed, initialize a new endpoint.
- Model Comparison: For an active deployment, compare the newly selected model against the current one in terms of traffic and performance metrics.
- Model Update: Should the new model outperform the existing deployment, proceed to update the endpoint with the selected model, ensuring optimal performance.
Implementing this pipeline via Vertex AI offers significant advantages:
- Seamless Automation: Reduces manual intervention in the model selection and deployment processes.
- Operational Efficiency: Leverages serverless architecture to minimize resource overhead and expedite executions.
- Enhanced Collaboration: Centralizes model contributions and evaluations, fostering a productive team environment.
- Continuous Monitoring: Incorporates ongoing performance tracking to maintain and enhance model accuracy.
This approach has been materialized into a Vertex AI Pipeline, enabling a serverless execution of the entire workflow. Each step of the outlined workflow is encapsulated into components that interact through defined inputs and outputs.
Pipelines represent workflows with multiple interdependent steps. Utilizing Vertex AI Pipelines, we can automate the model training, evaluation, and deployment process efficiently, treating each workflow step as a component within the pipeline.
- BQML Models: One or more BigQuery ML models that have been registered in the Vertex AI Model Registry.
Zachary Nguyen - @LinkedIn - zacharynguyen.ds@gmail.com
Project Link: https://github.com/zacharyvunguyen/End-to-End-Vertex-AI-Pipeline-for-Fraud-Detection