A structured approach to managing the lifecycle of artificial intelligence models, from development to deployment and ongoing monitoring, ensuring alignment with organizational objectives, regulatory requirements, and ethical considerations. This encompasses standardized processes, automation, and collaboration across various teams to facilitate the efficient and responsible use of AI. An example would be establishing a documented workflow for model retraining that includes bias detection and mitigation steps, coupled with automated deployment pipelines adhering to pre-defined performance benchmarks.
Its significance lies in fostering trust and transparency in AI systems, enabling organizations to scale their AI initiatives confidently while mitigating potential risks. Historically, ad-hoc model deployment led to inconsistent performance, compliance issues, and difficulties in tracking model lineage. By implementing a defined framework, organizations can improve model accuracy, reduce operational overhead, and ensure that AI systems are aligned with stakeholder values, avoiding unintended consequences and promoting responsible innovation.
