LLMOps & AI Infrastructure Roadmap — Step-by-step Learning Path
Operating LLM systems in production: deployment, monitoring, evaluation pipelines, cost control and reliability engineering for AI
The LLMOps & AI Infrastructure roadmap
- LLMOps Fundamentals — beginner
- Model Serving and Inference — beginner
- Model Serving Fundamentals — beginner
- Hosted APIs vs Self-Hosted Inference — beginner
- Inference Servers and Serving Frameworks — beginner
- Batching Requests for Throughput — beginner
- GPU vs CPU Inference Trade-offs — beginner
- CI/CD for AI Systems — beginner
- CI/CD Fundamentals for AI Systems — beginner
- Automated Testing in AI Pipelines — beginner
- Continuous Evaluation as Part of CI — beginner
- Deployment Pipelines for Prompts and Models — beginner
- Rollback Strategies for AI Releases — beginner
- Versioning Models, Prompts and Data — beginner
- Versioning Prompts — beginner
- Versioning Models and Model Configurations — beginner
- Versioning Training and Evaluation Data — beginner
- Reproducibility in AI Systems — beginner
- Change Management for AI Systems — beginner
- Monitoring LLM Systems in Production — beginner
- What to Monitor in an LLM System — beginner
- Quality Monitoring in Production — beginner
- Latency and Availability Monitoring — beginner
- Monitoring for Drift and Model Changes — beginner
- Building AI-Specific Dashboards — beginner
- Setting Alerting Thresholds for AI Metrics — beginner
- Building Evaluation Pipelines — beginner
- Automated Evaluation Pipeline Design — beginner
- LLM-as-Judge Evaluation Techniques — beginner
- Combining Automated and Human Evaluation — beginner
- Evaluation Dataset Management — beginner
- Regression Detection in Evaluation Pipelines — beginner
- Tracing and Observability for AI Systems — beginner
- Tracing Requests Through an AI Pipeline — beginner
- Structured Logging for AI Systems — beginner
- Building an AI Observability Stack — beginner
- Debugging Production AI Issues — beginner
- Privacy-Aware Logging for AI Systems — beginner
- Incident Management for AI Systems — beginner
- Incident Management Fundamentals for AI Systems — beginner
- On-Call for AI Systems — beginner
- Runbooks for Common AI Incidents — beginner
- Postmortems for AI System Failures — beginner
- Communicating AI Incidents to Stakeholders — beginner
- Reliability Engineering for AI Systems — beginner
- Reliability Engineering Principles for AI — beginner
- Defining SLOs for AI Features — beginner
- Error Budgets for AI Systems — beginner
- Redundancy and Failover for AI Dependencies — beginner
- Chaos Engineering for AI Systems — beginner
- Case Study: Improving Reliability of a Production AI System — beginner
- Scaling Inference Infrastructure — beginner
- Scaling Inference Horizontally — beginner
- Autoscaling for Variable AI Workloads — beginner
- Load Balancing Across Model Endpoints — beginner
- Multi-Region Inference Deployment — beginner
- Capacity Planning for AI Workloads — beginner
- Caching and Latency Optimization — beginner
- Caching Strategies for LLM Responses — beginner
- Semantic Caching — beginner
- Cache Invalidation for AI Systems — beginner
- Latency Optimization Techniques — beginner
- Measuring Cache Hit Rate Impact — beginner
- Cost Management and FinOps for AI — beginner
- FinOps Principles Applied to AI — beginner
- Cost Attribution Across Teams and Features — beginner
- Budget Alerts and Spend Controls — beginner
- Cost-Aware Model and Architecture Decisions — beginner
- Forecasting AI Infrastructure Costs — beginner
- Security and Compliance for AI Systems — beginner
- Security Fundamentals for AI Infrastructure — beginner
- Securing Model Endpoints — beginner
- Compliance Requirements for AI Systems — beginner
- Data Governance for AI Pipelines — beginner
- Auditing AI System Access and Usage — beginner
- Building an AI Security Review Process — beginner
- Model and Prompt Registries — beginner
- Model Registries for LLMs and Prompts — beginner
- Metadata and Lineage Tracking — beginner
- Approval Workflows for Registry Changes — beginner
- Discoverability Across Teams — beginner
- Deprecating Models and Prompts Safely — beginner
- Multi-Model Routing and Fallback — beginner
- Multi-Model Routing Fundamentals — beginner
- Fallback Chains Across Providers — beginner
- Cost-Based and Latency-Based Routing — beginner
- Provider Abstraction Layers — beginner
- Testing Multi-Provider Reliability — beginner
- A/B Testing and Experimentation Infrastructure — beginner
- A/B Testing Infrastructure for AI Features — beginner
- Feature Flagging for Prompts and Models — beginner
- Statistical Considerations for AI Experiments — beginner
- Shadow Deployments for Model Changes — beginner
- Experiment Result Analysis for AI Changes — beginner
- Career and Capstone — advanced