MLOps
Deployment, training pipelines, LLMOps, rollout, and lifecycle management for AI systems.
Core Concepts
| Concept |
Description |
| CI/CD for ML |
Pipelines that test data, models, and code before deployment |
| Model Registry |
Versioned store of trained models with metadata and lineage |
| Continuous Training |
Retraining on new data on a schedule or trigger |
| Canary Deployment |
Routing a fraction of traffic to a new model before full rollout |
| Rollback |
Reverting to a previous model version on regression |
| Data Versioning |
Versioning datasets alongside model versions for reproducibility |
| LLMOps |
MLOps practices specific to large language models: prompt versioning, eval gates |
Decision Guidance
| Situation |
Recommended Approach |
| First model deployment |
Simple pipeline: train → eval → register → deploy to staging |
| Frequent model updates |
Continuous training + canary rollout with automated eval gate |
| Multi-model serving |
Model registry + A/B testing framework |
| Regulated environment |
Full lineage: data version → model version → deployment record |
Anti-Patterns
| Anti-Pattern |
Fix |
| Deploying models without eval |
Add an eval gate before any production rollout |
| No model versioning |
Use a model registry; track data + code + config per version |
| Rollback requiring redeploy |
Keep previous model version hot; rollback is a config change |
| Training on unversioned data |
Version datasets; record data hash with each model |
- Deployment Architecture — Serverless, containers, edge, hybrid deployment
- Cost Optimization — Caching, model selection, token budgets
- Inference — Model serving and inference engines
- Observability — Monitoring deployed models
- Platform — Internal tooling for ML teams
Sources
- MLflow, Weights & Biases, Kubeflow, SageMaker Pipelines, Google Vertex AI, ML Engineering book, Chip Huyen, MLOps community, LLMOps papers.*
