Business_Understanding

Business Understanding Document: LLMOps Python Package

1. Executive Summary

This project provides a structured, multi-domain Python package for LLMOps (Large Language Model Operations). It implements standard practices for model registration, evaluation, and deployment using Domain-Driven Design (DDD) to ensure scalability in production environments.

2. Business Objectives

• Primary Goal: Standardize the lifecycle management of Large Language Models within the organization.
• Success Criteria:
  • Reduction in model deployment time.
  • Improved reproducibility of LLM experiments.

3. LLM SUCCESS METRICS

Note

Define metrics specific to the LLM's performance that align with business goals.

• Accuracy/Relevance: Target > 90% in contextual relevancy.
• Latency: P95 < 500ms for inference calls.
• Cost Efficiency: Optimize token usage via efficient prompting and caching.
• Safety/Bias: Zero tolerance for harmful content generation.

4. Stakeholders

Name	Role	Responsibilities
Data Science Team	Developers	Model training and evaluation logic.
MLOps Engineers	Infrastructure	CI/CD, Deployment, Monitoring.
Business Owners	Stakeholders	Defining KPIs and ROI.

5. REPOSITORY CONTEXT

Important

Link to relevant directories in the repository for business context.

• Project Root: Repo
• Configuration: Configs
• Documentation: Wiki

6. Solution Approach

The solution uses Autogen for agentic workflows and MLflow for experiment tracking and model registry. It follows a modular architecture separating domain logic from infrastructure.

7. Risks and Mitigations

Risk	Impact	Mitigation Strategy
Model Drifts	High	Implement continuous monitoring and automated retraining.
Hallucinations	Critical	Use grounding datasets and RAG-based architectures.

8. Project Plan & Milestones

• Phase 1: Planning: Q1 2026
• Phase 2: Development: Q2 2026
• Phase 3: Deployment: Q3 2026

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Template generated for Agentic workflows.