The 7 Phases

Define clear objectives, success metrics, ethical considerations, and risk analysis. This foundation ensures your AI project aligns with business goals.

Create project charter, collect requirements, identify data sources, and evaluate data quality. Build the foundation for your data strategy.

Clean, process, and enrich your data. Ensure quality assurance and handle data manipulation with care and governance.

Select pre-trained models, choose algorithms, execute training and optimization. Build your AI/ML solution with best practices.

Validate model performance, analyze underfitting/overfitting, review metrics, and ensure your solution meets quality standards.

Implement change management, deploy solution, validate with users, ensure compliance, and measure business impact.

Monitor production, measure success, manage versions, ensure governance, and drive continuous improvement.