MLOps and LLMOps: Two Sides of AI Operations
As large language models become central to AI applications, a new discipline has emerged: LLMOps. But how does it differ from traditional MLOps? And do you need separate tools for each?
What Is MLOps? (Quick Recap)
MLOps covers the operational lifecycle of traditional machine learning models:
- Data pipeline management
- Feature engineering and stores
- Model training and experiment tracking
- Model versioning and registry
- Deployment and serving
- Monitoring and retraining
MLOps has well-established patterns and tools that have been refined over the past decade.
What Is LLMOps?
LLMOps extends operational practices specifically for large language model workloads:
- Prompt management: Versioning, testing, and optimizing prompts
- Fine-tuning orchestration: Managing LoRA adapters, datasets, and training runs
- Evaluation: LLM-specific eval (hallucination detection, safety, coherence)
- Inference optimization: Quantization, caching, batching for generative models
- Cost management: Token-based billing, GPU utilization optimization
- Guardrails: Content filtering, safety checks, output validation
Key Differences
Data Pipeline
MLOps: Structured data ETL, feature engineering, train/test splits
LLMOps: Dataset curation for fine-tuning (instruction pairs), prompt template management, evaluation dataset creation
Training
MLOps: Full model training from scratch or transfer learning. Minutes to hours.
LLMOps: Fine-tuning with PEFT (LoRA, QLoRA) on pre-trained models. Hours to days.
Evaluation
MLOps: Standard metrics like accuracy, F1, MSE. Clear ground truth.
LLMOps: Subjective quality assessment, hallucination detection, safety testing, human evaluation. Harder to automate.
Deployment
MLOps: REST API serving with batch or real-time inference. Predictable latency.
LLMOps: Token-by-token generation, streaming responses, variable latency based on output length. GPU memory management critical.
Monitoring
MLOps: Data drift, prediction distribution, feature importance changes.
LLMOps: Output quality drift, hallucination rates, response length distribution, token costs, safety violations.
Cost Structure
MLOps: Training cost is one-time. Inference cost scales linearly with requests.
LLMOps: Training cost per fine-tuning run. Inference cost scales with both requests and output length. GPU costs dominate.
Where They Overlap
Despite the differences, MLOps and LLMOps share fundamental principles:
- Version everything: Code, data, models, configurations
- Automate pipelines: Reduce manual steps and human error
- Monitor continuously: Detect degradation before users do
- Test thoroughly: Validate before deploying to production
- Scale efficiently: Right-size infrastructure for demand
Do You Need Separate Tools?
Many organizations end up with separate tool stacks for ML and LLM workloads. This creates:
- Duplicate infrastructure costs
- Context switching between platforms
- Inconsistent practices across teams
- Integration overhead
The better approach is a unified platform that handles both MLOps and LLMOps.
SnapML: Unified ML and LLM Operations
SnapML by DeepQuantica is designed to handle both traditional ML and LLM workloads in a single platform:
For MLOps
- Auto ML for automated model building
- Experiment tracking and model registry
- One-click deployment for ML models
- Real-time monitoring with drift detection
For LLMOps
- Auto LLM for automated fine-tuning
- LoRA/QLoRA fine-tuning with monitoring
- Model playground for LLM evaluation
- Streaming API deployment with vLLM
- Token-level cost tracking
Shared Infrastructure
- Unified dataset management
- Single deployment pipeline
- Consistent monitoring and alerting
- Integrated API management
- One billing and access control system
Best Practices for Combined Operations
1. Use a unified platform: Avoid tool sprawl by choosing a platform that handles both ML and LLM workloads
2. Standardize experiment tracking: Same logging format for all model types
3. Share deployment infrastructure: Common container, scaling, and monitoring patterns
4. Domain-specific evaluation: Custom eval suites for each model type while using consistent frameworks
5. Unified governance: Same approval and audit processes for all production models
Conclusion
MLOps and LLMOps are converging. While LLMs have unique operational requirements, the foundational principles are the same. SnapML by DeepQuantica provides a unified platform that handles both, eliminating the need for separate tooling and ensuring consistent operations across all your AI workloads.