AI Development Lifecycle

AI is Different

AI development requires a different approach than traditional software. Success depends on data quality, iterative experimentation, and rigorous validation—not just clean code and good architecture.

Our AI development lifecycle is designed for the unique challenges of machine learning projects.

AI Process Phases

• Problem Definition — Define the ML problem and success metrics clearly
• Data Preparation — Collect, clean, and label training data
• Experimentation — Rapid iteration through models and approaches
• Validation — Rigorous testing on holdout data and real scenarios
• Deployment — Production serving with monitoring and feedback loops
• Continuous Improvement — Retrain and improve based on production data