AI Lifecycle Management and Workflow Design

Design and manage the full AI lifecycle from concept to decommission.

Pillar

Process – Workflow, Governance, Risk & Efficiency

Overview

This course provides a detailed framework for managing the entire lifecycle of Generative AI projects, from initial ideation through development, deployment, monitoring, and eventual retirement. Participants will learn to design workflows that ensure efficiency, quality, and compliance at every stage.

Learning Objectives

Participants will be able to:

• Understand key phases in the AI project lifecycle

• Design end-to-end workflows for GenAI development and deployment

• Implement best practices for monitoring and maintenance

• Manage risks and compliance throughout the lifecycle

• Plan for responsible decommissioning of AI models

Target Audience

• AI project managers and developers

• Data scientists and ML engineers

• Business analysts and product owners

• Compliance and risk management professionals

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Lectures and case studies

• Workflow design exercises

• Group discussions and scenario planning

Materials Provided

• Lifecycle management templates

• Workflow design tools and guides

• Compliance and risk management checklists

• Certificate of completion

Outcomes

• Create structured AI lifecycle workflows

• Ensure continuous quality and compliance

• Optimize resource allocation and project outcomes

• Safely retire AI models to mitigate risks

Outline / Content

Day 1: Understanding the AI Lifecycle

• Phases: ideation, development, deployment, monitoring, decommission

• Key challenges and considerations in each phase

• Aligning lifecycle with business goals

Day 2: Designing Workflows and Processes

• Workflow components and integration points

• Tools for workflow automation and orchestration

• Collaboration across teams and stakeholders

Day 3: Monitoring, Maintenance, and Risk Management

• Performance tracking and anomaly detection

• Model retraining and updating strategies

• Compliance and ethical considerations

Day 4: Decommissioning and Continuous Improvement

• Criteria and processes for retiring AI models

• Documentation and knowledge transfer

• Lessons learned and feedback loops

• Workshop: Design an AI lifecycle workflow for a GenAI use case