MLOps & LLMOps Interview Questions and Answers

Description

• MLOps and LLMOps Comparison
  

  Attribute	MLOps	LLMOps
  Primary focus	End-to-end ML model lifecycle: data, training, CI/CD, serving	Operationalizing large language models: prompt engineering, fine-tuning, RAG
  Primary users	Data scientists, ML engineers, DevOps	ML engineers, prompt engineers, platform teams
  Typical data	Labeled datasets, features, model artifacts	Massive text/code corpora, retrieval indices, user prompts
  Core components	Data versioning; experiment tracking; model registry; CI/CD; monitoring	Prompt/version control; fine-tuning; retrieval pipelines; cost & latency controls
  Advanced concerns	Reproducibility, drift detection, governance, automated retraining	Context window management, RAG orchestration, hallucination mitigation, cost optimization

  Description and Features from Basics to Advanced

  1. Definition — MLOps: MLOps is the discipline that applies DevOps principles to machine learning, enabling reproducible pipelines for data preparation, model training, testing, deployment, and monitoring.
  2. Definition — LLMOps: LLMOps is a specialized subset of MLOps focused on the operational challenges of large language models, including prompt engineering, fine-tuning, retrieval-augmented generation, and cost/latency management.
  3. Basic data practices: Both require data versioning, lineage, and validation to ensure training/serving parity; MLOps emphasizes labeled feature stores while LLMOps emphasizes curated corpora and retrieval indices.
  4. Experimentation and tracking: Core MLOps features include experiment tracking, hyperparameter management, and model registries to compare runs and promote artifacts to production.
  5. CI/CD for models: MLOps extends CI/CD with pipeline orchestration, reproducible environments (containers), automated tests for data and models, and gated deployments (canary/blue-green).
  6. Prompt and policy management: LLMOps adds prompt versioning, prompt templates, safety filters, and policy controls because small prompt changes can drastically alter outputs.
  7. Fine-tuning and adapters: Advanced LLMOps supports parameter-efficient fine-tuning (LoRA, adapters), instruction tuning, and continual fine-tuning pipelines to adapt foundation models to tasks.
  8. Retrieval Augmented Generation: LLMOps commonly integrates RAG pipelines—indexing, vector stores, retrieval strategies, and context assembly—to ground LLM outputs in external knowledge.
  9. Monitoring and observability: Production monitoring covers prediction quality, latency, resource usage, data drift, hallucination rates, and user-feedback loops; LLMOps also tracks prompt effectiveness and retrieval hit rates.
  • Bias, safety, and explainability: Mature pipelines include bias testing, content-safety filters, explainability tools, and audit trails for compliance and trust.
  • Cost and performance optimization: LLMOps must manage token costs, batching, model selection (distillation/quantization), and latency SLAs to make LLMs economically viable at scale.
  • Automation and closed loops: Advanced systems implement closed-loop automation where monitoring signals trigger retraining, prompt updates, or fallback strategies without manual intervention.
  • Security and access controls: Both require secure model registries, encrypted data stores, role-based access, and secrets management for safe production use.
  • Testing strategies: Beyond unit tests, MLOps uses data tests, model-slice tests, and shadow deployments; LLMOps adds scenario-based prompt tests, adversarial prompt testing, and hallucination benchmarks.
  • Scalability patterns: MLOps scales via distributed training, feature-store sharding, and autoscaling serving infra; LLMOps adds model sharding, offloading, and hybrid on-prem/cloud inference for very large models.
  • Governance and lineage: Advanced governance enforces model provenance, dataset lineage, approval workflows, and regulatory reporting for audits.
  • Tooling ecosystem: Typical MLOps tools include Kubeflow, MLflow, TFX, Seldon; LLMOps layers on vector stores, RAG orchestrators, prompt stores, and cost/usage dashboards.
  • Adoption path: Start with reproducible pipelines, telemetry, and basic CI/CD, then add monitoring, governance, and automated retraining for MLOps; for LLMOps, begin with prompt/version control and RAG, then add fine-tuning, safety, and cost controls.