Accelerated Innovation

Updating Your Model Registry

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Updating Your Model Registry

Description

This capability focuses on maintaining an accurate, up-to-date record of LLMs that have been evaluated, selected, or deployed. A model registry captures metadata such as model version, provider, evaluation results, approvals, usage status, and known issues.

Why it's Important

As LLM adoption expands, organizations need a single source of truth for tracking model decisions and ensuring governance. Without a maintained registry, it becomes difficult to manage updates, troubleshoot performance, ensure compliance, or share learnings. A well-managed model registry supports transparency, reusability, and enterprise-wide alignment.

Why it's Challenging @ Scale

  • Registries are often ad hoc or outdated: Many teams use spreadsheets or wikis that don’t reflect current usage or decisions.
  • Different teams track different attributes: Lack of consistency makes it hard to compare or combine entries.
  • Model updates are frequent: New releases, fine-tunes, and usage shifts require frequent updates.
  • Ownership is unclear: No one team may be responsible for maintaining or validating registry accuracy.
  • Integration with workflows is limited: When registries are separate from build or governance tools, they are often skipped or forgotten.

Complexity

Medium to High: Maturing this capability requires a centralized system, clear metadata standards, workflow integration, ownership accountability, and alignment with compliance and evaluation processes.

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Taking Action

Though most organizations begin their GenAI journey with significant knowledge gaps, there are targeted actions that can be taken to accelerate the process. Select your group’s current maturity, based on your assessment results, and act today.

  • Explore Key Concepts & Best Practices: Complete the Evaluating and Selecting the Best Model(s) for Your GenAI Solution workshop (2 hrs.) to understand foundational key concepts and explore applied best practices
  • Outlining the Model Evaluation Lifecycle
  • Understanding Model Types and Capabilities
  • Aligning Evaluation to Solution Objectives
  • Comparing Commercial vs. Open Source Options
  • Establishing a Reusable Evaluation Framework
  • Define Your Action Plan: Outline concrete, prioritized steps your organization will take to implement GenAI Strategy
  • Align on your Current State and define your Target State
  • Create an actionable enablement plan
  • Define target timeline and measures of success
  • Deliver Quick Wins: Small, high-impact GenAI projects that can demonstrate tangible value in a short time frame
  • Create a Lightweight Registry Template: Include key fields like model name, provider, task, evaluation status, and approval.
  • Log Your First 2-3 Models: Capture current state info for models used or tested in early pilots.
  • Assign Registry Ownership: Identify who is responsible for maintaining updates and data quality.
  • Complete one or more of our Deep Dive Courses: Begin exploring key concepts and best practices, including:
  • Defining Your Model Objectives & Requirements
  • Model Evaluation Data Assessment and Prep
  • Selecting In-Scope Models
  • LLM Evaluation
  • Nail It Before You Scale It: Assess and optimize your solution or process before adopting it at scale
  • Assess Your Proposed Solution or Process: Review whether your current registry captures the right metadata for decision-making.
  • Define in-scope Processes and Guardrails: Standardize what qualifies as a “registered model” and which fields are mandatory.
  • Close any Data or Measurement Gaps: Backfill missing info for existing models and confirm evaluation results are linked.
  • Define Your Adoption & Scaling Plan: Create a structured roadmap for how GenAI solutions will be rolled out across teams, workflows, or business units
  • Define Your Phased Implementation Plan: Expand the registry from pilot use cases to broader teams and workflows.
  • Build Awareness and Finalize Enablers: Train teams on when and how to use the registry as part of GenAI development.
  • Operationalize Your Comms Plan: Share visibility into how the registry will be used, who can access it, and what it supports.
  • Formalize Your Best Practices: Document and standardize what’s working to ensure consistent, scalable success across teams and use cases
  • Define Registry Field Standards: Set naming conventions, required metadata fields, and validation logic.
  • Publish Registry Governance Guidelines: Outline responsibilities, update processes, and review cadences.
  • Integrate Registry with Evaluation Workflows: Ensure every evaluated model automatically generates a new or updated registry entry.
  • Accelerate Your Adoption: Intensify efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers
  • Enable Self-Service Registry Updates: Let users add or edit entries with guardrails and approvals in place.
  • Connect Registry to Model Access Tools: Tie registry status to who can use, deploy, or modify a model.
  • Promote the Registry as a Discovery Tool: Help teams find already-approved models that match their use case.
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum
  • Highlight Registry-Driven Reuse: Showcase when teams avoided duplicative work by checking the registry first.
  • Recognize Registry Maintainers: Acknowledge those keeping entries accurate, current, and accessible.
  • Visualize Registry Growth: Share trends in model entries, metadata coverage, or adoption across teams.
  • Streamline & Embed: Integrate GenAI into core workflows while eliminating friction points to make usage seamless and routine
  • Embed Registry Updates into DevOps Pipelines: Auto-update the registry when models are deployed or re-evaluated.
  • Link Registry Entries to Compliance and Risk Tools: Centralize approvals, usage logs, and risk notes alongside model metadata.
  • Build Search and Recommendation Features: Help teams find the best-fit model based on filters, tags, or past evaluations.
  • Leverage Automation: Using GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort
  • Auto-Flag Stale or Incomplete Entries: Notify owners when models are outdated or missing key information.
  • Use LLMs to Draft Registry Summaries: Summarize model purpose, status, and strengths based on existing fields and evaluations.
  • Integrate Registry with Access Management: Grant or restrict model usage automatically based on registry status.
  • Evolve & Further Accelerate: Continuously refine GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases
  • Track Model Lineage Over Time: Show relationships between base models, fine-tunes, and experimental forks.
  • Expand Registry to Include Prompts and Templates: Provide full context for how models are used in production.
  • Audit Registry Health Regularly: Monitor usage, accuracy, and completeness as part of governance reporting.

Key "Watchouts"

As you take action you’ll want to avoid:

  • Letting the registry go stale: A registry that isn’t regularly updated quickly becomes untrustworthy.
  • Tracking too much or too little: Overcomplicating the schema can slow adoption, while missing fields reduce value.
  • Leaving updates to chance: Without clear ownership and process, updates may be inconsistent or forgotten.
  • Allowing duplicate entries: Lack of validation can lead to confusion and rework.
  • Failing to align with governance: If the registry isn’t integrated into review and approval workflows, it won’t support compliance.

Targeted Benefits

While Updating Your Model Registry can be challenging, its benefits are clear and compelling, including:

  • Improved model governance: Centralized tracking supports audits, compliance, and risk mitigation.
  • Faster reuse and adoption: Teams can find approved models quickly without starting from scratch.
  • Greater transparency and traceability: Stakeholders can see who evaluated or approved a model, and why.
  • Stronger cross-team collaboration: Shared visibility encourages consistency across data science, IT, and business users.
  • Scalable model operations: A maintained registry supports growth in model volume, complexity, and business impact.

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Eddie
Accelerated Innovation

Hi, I'm Eddie 👋

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