AI Infrastructure
infrastructure
What Is MLOps / LLMOps?
AsterMind Team
MLOps (Machine Learning Operations) is a set of practices that combines machine learning, DevOps, and data engineering to deploy, monitor, and maintain ML models in production reliably and efficiently. LLMOps extends these practices specifically for large language model workflows, including prompt management, RAG pipeline operations, and LLM-specific monitoring.
Why MLOps Matters
Most ML models never make it to production. The gap between a successful experiment and a reliable production system is enormous:
- 87% of ML projects never make it past the experimental phase
- Models degrade over time due to data drift
- Reproducing experiments without proper tracking is nearly impossible
- Manual deployments are slow, error-prone, and unscalable
The MLOps Lifecycle
1. Data Management
- Data versioning and lineage tracking
- Feature engineering and feature stores
- Data quality monitoring and validation
2. Model Development
- Experiment tracking (hyperparameters, metrics, artifacts)
- Model versioning and registry
- Reproducible training pipelines
3. Deployment
- Model packaging and containerization
- A/B testing and canary deployments
- Model serving infrastructure (batch and real-time)
4. Monitoring
- Model performance tracking (accuracy, latency, throughput)
- Data drift detection
- Alerting and automated retraining triggers
5. Governance
- Model documentation and audit trails
- Bias detection and fairness monitoring
- Compliance reporting
MLOps vs. LLMOps
| Aspect | MLOps | LLMOps |
|---|---|---|
| Models | Custom-trained models | Pre-trained LLMs + fine-tuned variants |
| Training | Full training pipelines | Fine-tuning, RLHF, prompt optimization |
| Key Metrics | Accuracy, precision, recall | Quality, latency, cost per query, hallucination rate |
| Data Management | Training datasets | Prompt templates, RAG knowledge bases |
| Deployment | Model serving | API gateway, caching, rate limiting |
| Cost Focus | GPU training costs | Per-token inference costs |
Key MLOps Tools
| Category | Tools |
|---|---|
| Experiment Tracking | MLflow, Weights & Biases, Neptune |
| Model Registry | MLflow, SageMaker, Vertex AI |
| Orchestration | Kubeflow, Airflow, Prefect |
| Feature Store | Feast, Tecton, Hopsworks |
| Serving | TensorFlow Serving, Triton, BentoML |
| Monitoring | Evidently, Arize, WhyLabs |