The ML life cycle
Reproducibility is crucial in ML to ensure consistent and replicable results. Integrating the MLOps life cycle promotes reproducibility, auditability, ethics, reliability, and trustworthiness in ML models. Auditability ensures the model behaves as expected and provides transparency of its workings and data usage. Ethical guidelines addressing privacy rights, data accuracy, and transparency are essential when deploying AI systems. A well-defined ML life cycle facilitates adherence to these guidelines. Model monitoring guarantees reliability by operationalizing the trustworthiness of ML models, particularly in high-risk applications and regulated industries. Implementing routine testing, validation by outside experts, and ongoing performance monitoring is essential. Clear explanations for decision-making and user control over their own data ensure a transparent, repeatable, and auditable AI system.
Adopting an ML life cycle
ML operationalization is the process...
