The paper, "An Artificial Intelligence Life Cycle: From Conception to Production," provides a comprehensive framework titled the "CDAC AI Life Cycle" for the structured development of AI systems, developed by authors Daswin De Silva and Damminda Alahakoon. Drawing from extensive experience in AI research, industry, and education, the authors outline an AI life cycle that encompasses three core phases—Design, Develop, and Deploy—each consisting of detailed stages spanning across the conceptualization to production of AI initiatives.
Design Phase
The Design phase emphasizes the importance of problem contextualization and ethical considerations in AI system development. This phase involves:
- Problem Identification and Formulation: Defining the problem with a contextual understanding of data environments and existing solution flows.
- AI Literature Review: Surveying existing ethics guidelines, AI algorithms, and pre-trained models relevant to the problem domain.
- Data Preparation and Exploration: Ensuring data integrity through single-source-of-truth approaches, data warehousing, and benchmarking against industry standards.
- External Data Acquisition: Navigating short-term data procurement from brokers or public sources, while ensuring compliance with ethical and regulatory standards.
Develop Phase
The Develop phase is technique-focused, transforming data into functional AI models:
- Data Wrangling and Augmentation: Addressing class imbalances and performing advanced feature engineering.
- Model Building and Benchmarking: Developing initial models and establishing performance baselines. Techniques such as transfer learning with pre-trained models are recommended.
- Model Expansion and Evaluation: Iteratively refining AI models through parameter tuning, and evaluating using metrics like accuracy and bias-variance trade-off.
- Explainability (XAI): Applying intrinsic and extrinsic methods, such as PDP, LIME, and SHAP, to enhance model interpretability.
Deploy Phase
The Deploy phase is oriented towards operationalization with a focus on computational effectiveness and system integration:
- Model Deployment: Deciding on efficient deployment strategies considering real-time requirements and end-user demands.
- MLOps/AIOps: Leveraging AI pipelines for model deployment, incorporating technologies like containers and microservices to streamline and automate processes.
- Hyperautomation: Integrating AI capabilities with automated components of business processes to foster innovation and efficiency.
- Continuous Performance Monitoring: Evaluating model drift, end-user engagement, and return on investment to ensure sustained model efficacy.
Ontological and Organizational Mapping
Further contributions of the paper include an ontological mapping of AI algorithms to applications, simplified into four primary capabilities: Prediction, Classification, Association, and Optimization. This mapping aids communication between AI practitioners and organizational stakeholders. The life cycle's organizational context positions AI activities within the broader strategy-to-decision-making workflow of organizations, aligning technical functions with corporate objectives.
Conclusion
The CDAC AI Life Cycle is posited as a tool to enhance awareness, knowledge, and transparency in AI system development. By integrating AI development with organizational strategy, it aims to facilitate informed discussions, ensuring AI initiatives align with strategic goals. The ontological mapping and organizational context enrich the dialogue between technical experts and business leaders, emphasizing AI's role in transformative value creation across industry sectors.