AI Model Development SOP

This detailed SOP provides data scientists, machine learning engineers, and project managers with a structured framework for managing AI model development within an organisation.
It includes:
– Step-by-Step Process Flow: Outlines essential subprocesses such as Data Collection, Model Selection, Model Evaluation, and Model Deployment and Monitoring, with clear actions, decision points, and feedback mechanisms to ensure the successful creation and implementation of AI models.  
– Risk Management: Identifies key risks such as poor data quality, misaligned model objectives, and deployment failures, with mitigation strategies including thorough data validation, stakeholder alignment, and comprehensive deployment testing to minimise risks and optimise model performance.  
– Compliance and Regulatory Requirements: Ensures adherence to relevant regulations such as GDPR, intellectual property laws, and ethical AI guidelines by integrating compliance checks throughout the data collection, model development, and deployment processes to safeguard data privacy and legal compliance.  
– Key Performance Indicators (KPIs) and Controls: Defines KPIs such as data quality assurance, model performance metrics, and stakeholder engagement scores, with controls like data validation checks, algorithm selection documentation, and deployment risk mitigation plans to ensure continuous improvement and alignment with business goals.  
– RACI Framework: Clearly defines roles and responsibilities for each task in the AI model development process, ensuring that project managers, data scientists, stakeholders, and IT teams are accountable and engaged at every stage.  
– Systems Requirements: Details the necessary systems, including Data Collection Platforms, Model Training Environments, Model Evaluation Tools, and Deployment Management Systems, to support the AI model development process and ensure secure, efficient, and effective management. 
– Appendices: Provides practical resources such as data collection checklists, model evaluation templates, and case studies to guide users through each stage of the AI model development process effectively.