AI Services & MLOps
AI product development, LLM integration, MLOps infrastructure, and AI workflow automation
Deliverables
LLM Integration Architecture
Complete architecture for production LLM deployments with error handling, fallbacks, and cost optimization
- -Anthropic Claude integration via AWS Bedrock and direct API
- -Multi-model routing with fallback chains (Claude Opus → Sonnet → Haiku)
- -AWS Bedrock deployment with IAM policies and VPC endpoints
- -Retry logic, circuit breakers, and rate limiting
- -Streaming responses for improved user experience
Prompt Engineering Framework
Version-controlled prompt management system with A/B testing and optimization workflows
- -Prompt template library with versioning
- -A/B testing framework for prompt optimization
- -Performance metrics and cost tracking per prompt variant
MLOps Pipeline Setup
End-to-end MLOps infrastructure on AWS with automated training, deployment, and monitoring
- -SageMaker training and inference pipelines
- -Model registry and versioning system
- -Automated drift detection and retraining triggers
LLM Observability Dashboard
Comprehensive monitoring for cost, performance, and quality metrics across all AI services
- -LangSmith/Helicone integration
- -Cost tracking by feature and user segment
- -Latency and token usage dashboards
Key Questions
(17 questions)Are Anthropic Claude models integrated via AWS Bedrock or direct API with proper error handling?
Are LLM integrations (OpenAI, Anthropic, Bedrock) implemented with fallback chains and retry logic?
Is prompt engineering documented with version control and A/B testing for optimization?
Are Claude-specific features utilized (extended context, tool use, vision capabilities)?
Are AI workflows automated with orchestration tools (LangChain, LangGraph, agents, function calling)?
Are custom subagents or skills built for domain-specific workflows?
Is LLM observability configured (LangSmith, Helicone, custom logging) for cost and performance?
Is MLOps infrastructure set up on AWS (SageMaker, EKS for inference, model registry)?
Are ML models containerized and deployed with proper scaling (Kubernetes HPA, SageMaker endpoints)?
Is model performance monitored for drift detection, accuracy degradation, and retraining triggers?
Are AI/ML development environments standardized (notebooks, dev containers, reproducible setups)?
Are AI-assisted development tools adopted (Cursor, GitHub Copilot, Claude Code) across the team?
Are Claude Code plugins or custom skills developed to accelerate team workflows?
Is codebase context optimized for AI tools (CCS, good documentation, clear structure)?
Are AI sprint sessions conducted to prototype and validate ideas rapidly?
Is there a strategic AI roadmap aligned with business objectives and customer needs?
Are AI capabilities differentiated from competitors with proprietary approaches or data?
Artifacts To Review
Sample Outputs
LLM Integration Assessment Report
Detailed analysis of current LLM usage with cost optimization recommendations and production readiness checklist
Prompt Engineering Playbook
Curated prompt templates, versioning workflow, and A/B testing framework tailored to your use cases
MLOps Maturity Roadmap
6-12 month roadmap for advancing from basic model deployment to full MLOps
AI Observability Dashboard
LangSmith or custom Grafana dashboard tracking LLM costs, latency, token usage, and quality metrics
Maturity Levels
Basic LLM API usage with hardcoded prompts, no monitoring or cost tracking
LLM integrations with error handling, basic prompt versioning, manual model deployment
Systematic prompt engineering, LLM observability, automated ML deployment pipelines, cost tracking
Production-grade MLOps with drift detection, automated retraining, comprehensive observability, AI-assisted workflows across team
Get AI Services & MLOps Insights
Schedule a discovery call to discuss how this assessment can help your organization. Fractional CAIO clients receive this module included in their retainer.