CDAC AI Life Cycle Framework

• CDAC AI Life Cycle is a comprehensive framework that guides the design, development, and deployment of AI solutions while integrating technical rigor and ethical governance.
• It segments the process into Design, Develop, and Deploy phases with 17 stages, ensuring structured model evaluation and continuous improvement.
• The framework facilitates cross-disciplinary collaboration, operational automation via MLOps/AIOps, and robust performance monitoring for adaptive AI integration.

The CDAC AI Life Cycle is a comprehensive, stepwise framework for the design, development, and deployment of artificial intelligence systems and solutions, emphasizing the integration of technical rigor, ethical considerations, and organizational alignment. It establishes a sequence of 17 constituent stages grouped into three high-level phases—Design, Develop, and Deploy—mapping the process from conception to production. The life cycle's architectural and methodological elements are intended to ensure principled contextualization of AI problems, robust model building and evaluation, reliable operationalization, and continual post-deployment improvement (Silva et al., 2021).

1. Framework Structure and Phase Organization

The CDAC AI Life Cycle divides activities into three main phases (Silva et al., 2021):

Design phase: Focused on contextualizing and framing the AI problem.

• Activities include problem identification, literature review (including ethics and state-of-the-art models), and data acquisition/preparation.
• Emphasizes consolidation of data assets into a "Single Version of Truth" (SVOT) via data warehouses, lakes, or lakehouses, with explicit consideration for regulatory compliance, stewardship, and ethical governance.

Develop phase: Centers on the technical transformation of data and algorithmic insights into AI models.

• Activities involve initial model development, benchmarking, iterative improvements (complexity and parameter tuning), evaluation via primary (accuracy, sensitivity) and secondary (computational efficiency, convergence) metrics, and model explainability using intrinsic/extrinsic XAI techniques (e.g., PDP, ICE, LIME, SHAP).

Deploy phase: Concerns operationalization, integration, and continuous monitoring of the AI solution.

• Includes model serving/scoring (batch/real-time), pipeline automation using containers/microservices (MLOps/AIOps), hyperautomation (integrating AI into system-wide automation), and ongoing monitoring for drift and staleness.

Each phase integrates subject-matter expertise: Design (AI/data scientist), Develop (AI/ML scientist), Deploy (AI/ML engineer).

2. Detailed Stage Progression and Ontological Mapping

Seventeen constituent stages are distributed across the three phases, each characterized by distinct outputs and stakeholder involvement. The process begins with the precise definition of the problem, contextual review of literature (including pre-trained models and ethics frameworks), and concludes with robust integration and monitoring in deployed environments (Silva et al., 2021).

Stages can be mapped ontologically:

• Four primary capabilities—Prediction, Classification/Detection, Association, and Optimisation—serve as intermediaries between abstract algorithms and domain-specific applications.
  • Prediction: E.g., regression, time-series forecasting.
  • Classification/Detection: E.g., anomaly detection, object identification.
  • Association: Clustering, dimensionality reduction.
  • Optimisation: Scheduling, control, planning, simulation.

This mapping supports optimal selection of algorithms for specific problem domains and fosters alignment between technical teams and domain stakeholders.

3. Methodologies and Iterative Processes

The methodology enforces a sequential but highly iterative approach:

• Feedback loops are embedded throughout, allowing cycles of re-formulation, re-annotation, model revision, and deployment recalibration as new knowledge or data become available.
• Early project termination is encouraged when feasibility and data availability criteria are not met.
• Model evaluation incorporates both classic metrics (accuracy, Dice score for segmentation) and domain-specific post-processing (e.g., geometric fitting for clinical measurement extraction (Lu et al., 2020)).
• Deployment practices include "shadow inference"—executing models on live data without affecting core decisions prior to full integration—promoting safe and evidence-driven transitions (Lu et al., 2020, Steidl et al., 2023).

A typical cycle includes:

1. Feasibility and impact assessment
2. Data acquisition, cohort selection, and cleaning
3. Data annotation and labeling (with inter-annotator consistency checks)
4. Model exploration, architectural refinement, and hyperparameter tuning
5. Model testing (holdout, cross-validation, clinician-driven evaluation)
6. Acceptance/iteration (based on pre-established criteria)
7. Deployment and continuous monitoring

4. Operationalization, Automation, and Continuous Monitoring

Modern CDAC practices are deeply integrated with automated pipeline management frameworks:

• MLOps/AIOps extend the traditional DevOps paradigm, incorporating data and model versioning, automated testing, CI/CD practices for AI, and workflow orchestration (Steidl et al., 2023).
• Automated performance prediction and KPI analytics reduce manual effort in pre-release testing, monitoring, and model improvement (Arnold et al., 2020).
  • Calibrated confidence scores are aggregated to estimate model accuracy on unlabeled production data.
  • KPI analytics correlate AI outputs with business metrics, ensuring that monitoring is contextually meaningful and tightly coupled to organizational goals.

System operations perpetuate feedback from runtime environments directly to earlier stages, supporting rapid retraining, rollback, and pipeline adaptation as required by observed metrics or alerts.

5. Organizational Alignment and Contextual Integration

A core tenet is organizational alignment: the life cycle stages are mapped onto enterprise functions (Silva et al., 2021).

• Technical phases (Design, Develop, Deploy) correspond with broader business strategy and execution functions, promoting integration of AI solutions within corporate governance, risk, compliance, and operations.
• Stakeholder engagement extends from executive management to cross-functional technical teams, reinforcing that AI adoption is neither isolated nor purely technical, but linked to strategic imperatives and regulatory frameworks.

Documentation is maintained as a continuously updated and peer-reviewed artifact, ensuring traceability across all decisions, model changes, and data lineage—a practice acknowledged as crucial in regulated domains (Haakman et al., 2020).

6. Lessons from Domain-Specific Case Studies

Empirical applications in healthcare and fintech demonstrate the life cycle’s adaptability and efficacy:

• In clinical AI (e.g., aortic aneurysm detection from CT exams), three major iterations included data curation, annotation/training for both abdominal and thoracic regions, and an engineered routing mechanism for model deployment (Lu et al., 2020).
  • Strong performance metrics (Dice ≥ 0.90, sensitivity/specivity ≥91/95%) were achieved by integrating rigorous annotation protocols and morphological post-processing.
• In regulated financial environments, explicit identification of data collection, feasibility, documentation, risk assessment, and monitoring stages improved overall reliability, transparency, and compliance (Haakman et al., 2020).

Iterative refinement, inter-disciplinary technical-domain collaboration, robust annotation quality checks, and integrated post-processing are consistently highlighted as critical success factors.

7. Relation to Other Lifecycle Models and Standardization Efforts

Comparative studies and systematic mappings underscore that CDAC advances beyond traditional models (CRISP-DM, TDSP, standard SDLC) in several respects:

• Emphasis on holistic, end-to-end lifecycle management rather than isolated technical phases (Xie et al., 2021).
• Explicit elevation of underrepresented aspects such as data traceability, versioning, risk management, and organizational alignment.
• Integration with regulatory and agile methodologies allows adaptation for highly regulated environments (e.g., RegOps for AI-enabled medical devices (Granlund et al., 12 Sep 2024)).
• Continuous feedback, monitoring, and adaptive retraining ensure resilience to concept drift, changing environments, and evolving stakeholder requirements (Huang et al., 24 Jul 2025).

The CDAC AI Life Cycle thus represents an adaptable, rigorously structured approach to AI solution development and deployment, balancing technical, ethical, operational, and organizational considerations across a comprehensive sequence of methodological stages.