Decommissioning AI Systems: Best Practices and Guidelines for Off-boarding Large Language Models and Infrastructure

As organizations increasingly depend on artificial intelligence systems, knowing when and how to properly decommission these systems is vital. This process involves more than just technical steps; it requires careful attention to data storing and archiving regulations, in conjunction with effective operational practices. In this article, we will look at best practices for off-boarding large language models (LLMs), apply machine learning principles during decommissioning, and explore legal implications tied to data storage and management. More importantly, what criteria is used to best identify and determine a system requires deprecation?

In a previous article, "Establishing Ethical AI Governance in Responsible and Trustworthy AI Development", we made mention of the AI Lifecycle and a few governing principles and organizations. Protecting your users information is not limited to the "in use" workflows and networking to support traffic. It includes how the underlying infrastructure, data, and retention policies are handled post deprecation to ensure the security of relevant information. We've also touched on the continued adoption of cloud computing capabilities within the government, healthcare, legal, and financial industries. Security related capabilities as it relates to consumer data has never been more important than now.

Understanding the Context of Decommissioning AI Systems

Decommissioning means discontinuing the operation of AI systems, including Large Language Models (LLMs) or other machine learning models. As more executives, engineers and developers shift towards new innovative advancements in AI, its important to take this step into consideration raising awareness of different capabilities or processes when implementing and deprecating LLMs. Organizations need a solid understanding of when and how to retire a system that is no longer effective. For instance, a study showed that 70% of organizations face compliance challenges when managing outdated AI systems.

The need for decommissioning can stem from various factors, such as operational efficiency, risk management, and adherence to data regulations like the General Data Protection Regulation (GDPR). The process can become complex, influenced by the infrastructure used, the nature of the data processed, and the management of hardware resources such as GPUs, TPUs, and advanced heating and cooling systems.

When to Deprecate Models

AI systems are considered as "always on". This means they are fully capable of performing the intended tasks, around the clock and without interruption or degraded performance. However, when a system is no longer able to achieve the intended goals for it, deprecation or decommissioning is taken into consideration. Determining when to deprecate a model is crucial for keeping an efficient AI system. Here are key signs that a model may be ready for retirement:

• Performance Decline: A model showing a 15% drop in accuracy over a quarter may indicate it is time to consider decommissioning. This raises awareness of the importance of implementing performance metrics, such as Service Level Indicators (SLIs) and Service Level Objectives (SLOs) to track and enhance performance or utilizing toolkits such as AI Verify, measures AI system performance using industry standardized tests validated by internationally recognized principles.

  
  

• Data Drift: If data patterns shift significantly, such as a 20% change in the input characteristics, the model may lose its effectiveness. When the distribution of data input remains ongoing, with an inability to successfully modify the algorithms used to make accurate predictions.

  
  

• Compliance Issues: New regulations might result in certain models being non-compliant, requiring retirement once updating is no longer an option.

  
  

• Technological Advancements: The introduction of new algorithms can often improve output quality by over 30%, making older models less effective. This includes new advancements that require the implementation of a better solution resulting in reduced resource allocation for maintaining the initial LLM.

  
  

Having a systematic approach to review the performance of models regularly can assist organizations in making relevant and informed deprecation decisions.

Best Practices for Off-boarding Large Language Models

1. Establish a Clear Deprecation Policy: Create and document a formal protocol for decommissioning AI systems. This should detail criteria for retirement, processes for transitioning to new systems, and the roles of team members involved.

   
   

2. Data Management and Compliance: Ensuring compliance with data management laws is crucial when decommissioning models. For example, if a model used customer data, that data must be handled in accordance with GDPR, including how it is deleted or anonymized.

   
   

3. Documentation and Audit Trails: It's important to document each step taken during the decommissioning process. Keep an audit trail that lists the data used, decisions made, and steps executed. This transparency aids compliance and facilitates future evaluations.

   
   

4. Stakeholder Communication: Actively communicate with relevant stakeholders throughout the decommissioning process. This helps manage expectations and ensures a smoother transition to new systems or models.

   
   

5. Archiving and Storage Policies: Define which data and model versions need archiving. Follow a storage policy compliant with regulations, considering what metadata may help in future audits before final deletion of any data.

   
   

Storage-Related Policies and Legal Implications

Organizations must create effective data storage policies to comply with regulations like GDPR. This law requires organizations to have a clear justification for data collection, limit retention periods, and ensure appropriate data removal when it's no longer necessary.

Global Data Management Regulations

Understanding the impact of global regulations on data management is crucial during the decommissioning process.

• GDPR Compliance: The GDPR necessitates that businesses provide a solid rationale for data collection and have a mechanism to delete or anonymize data when necessary. For modest businesses, compliance with these regulations can help avoid fines that can reach up to 4% of annual revenue.

  
  

• Data Localization: Certain regions impose data localization laws that require data to be processed within specific geographical areas. For example, the new laws in several Asian countries mandate that data must reside within their borders, directly affecting cloud storage strategies during decommissioning.

  
  

Actively ensuring compliance is critical to your organization to reduce the potential risks associated with data breaches or penalties brought on by an ineffective decommissioning process.

Infrastructure (Hardware) Implications: GPUs and TPUs

Decommissioning also involves examining the hardware architecture and setup within the relevant data centers. Many organizations utilize specialized processors, AI accelerators and memory units within their hardware architecture. Graphics Processing Unit (GPUs) and Tensor Processing Unit (TPUs) become more relevant rather than commonly known Central Processing Unit (CPUs), for training and model inference.

Energy Considerations

During transitions or decommissioning, organizations must evaluate the energy usage of these components. For instance, high-performance GPUs can lead to a 40% increase in energy costs. When services are discontinued:

• Assess Cooling Requirements: Existing cooling systems need to accommodate active hardware without overloading.

  
  

• Sustainability Practices: Weigh energy-efficient practices and hardware recycling options against your corporate sustainability goals.

  
  

Cost Implications

Changing hardware can incur direct costs, like purchasing new equipment, or indirect costs, such as the effect on productivity. Conduct a thorough cost-benefit analysis to understand the financial impact of decommissioning AI infrastructure.

AI Principles During Decommissioning

It's essential to maintain ethical standards and privacy during the decommissioning of AI systems, related to Large Language Models (LLMs), incorporating machine learning principles throughout the process.

1. Responsible Usage Guidelines: Data and insights obtained from models must comply with ethical standards long after their deletion. It's important to implement policies surrounding retention periods after deletion and the manner of which its appropriate to delete relevant data.

   
   

2. Bias and Fairness: Address and remove any potential biases in reference materials when the model is retired, ensuring they do not influence future operations.

   
   

3. User Impact Consideration: Evaluate how the decommissioning process might affect users or stakeholders. Develop strategies that minimize disruption during the transition. This raises awareness of the importance of design documentation in the early phases and maintaining records of relevant metadata related to an AI system throughout its lifecycle.

Final Thoughts

Decommissioning AI systems, especially large language models, presents various technical, operational, and legal challenges. By following the best practices and guidelines in this post, organizations can effectively navigate the complexities involved in offboarding AI systems.

From recognizing indicators for model deprecation to understanding data storage implications, these considerations serve as a roadmap for ensuring compliance while optimizing resource management. As AI systems continue to advance, so will the strategies for managing their lifecycle, highlighting the need for systematic and principled decommissioning processes.

This guide will be particularly valuable for AI engineers, DevOps specialists, infrastructure engineers, and AI architects committed to managing AI resources responsibly.