MLOps & LLMOps interview prep

• Agent Loop Prevention — Preventing agents from infinite loops when tools fail — retry state counters in Pydantic state models, circuit
• Agent Tool Security — Securing agent access to internal data sources — SQL injection prevention, MCP tool definitions, JWT-scoped pe
• Azure Machine Learning Platform — End-to-end ML lifecycle management using Azure ML workspaces, managed endpoints, MLflow integration, and Respo
• Data Versioning and Dataset Management — Git-like versioning for large datasets and data lakes using DVC, Delta Lake, Apache Iceberg, and LakeFS.
• DevOps to MLOps Transition — MLOps extends DevOps principles to machine learning by treating data, models, and pipelines as first-class ver
• Feature Stores — Centralized service for computing, storing, and serving features for both training and inference — eliminates
• Hyperparameter Tuning and AutoML — Hyperparameter tuning systematically searches for optimal model configuration values that cannot be learned fr
• LLM Serving — LLM inference is unique — KV cache, continuous batching, GPU memory bound. Covers vLLM (PagedAttention), Huggi
• LLMOps — LLMOps as the LLM-shaped subset of MLOps — prompt versioning, evaluation harnesses, RAG observability, cost/to
• ML Experiment Tracking — ML experiment tracking is the practice of systematically recording hyperparameters, metrics, artifacts, and en
• ML Governance and Compliance — ML governance establishes accountability, traceability, and auditability across the full model lifecycle. Comp
• ML Monitoring and Alerting — ML monitoring tracks both infrastructure health and model performance over time, using drift detection and ale
• ML Pipeline Orchestration — Designing and running reproducible ML workflows with Kubeflow Pipelines, Airflow, Prefect, Dagster, and Metafl
• ML Security and Adversarial Robustness — Adversarial attacks, data poisoning, model stealing, supply chain risks, and defenses including adversarial tr
• ML Testing Infrastructure — Building a complete ML test pyramid covering data validation, behavioral model tests, pipeline determinism, an
• MLOps CI/CD Automation — MLOps CI/CD automation applies continuous integration and delivery principles to machine learning pipelines, e
• MLOps Lifecycle — End-to-end machine-learning lifecycle as an engineering discipline — data ingestion, feature engineering, trai
• Model Deployment Strategies — Blue/green, canary, shadow, A/B, and feature flag strategies for safely promoting ML models to production.
• Model Drift Detection — Detecting when production models degrade — covariate shift, concept drift, label drift, prior probability shif
• Model Evaluation — Offline and Online — Rigorous offline validation and controlled online experiments to safely promote ML models to production.
• Model Packaging and Formats — Model packaging converts a trained model into a portable, reproducible artifact that can be deployed consisten
• Model Registry — Centralized versioned store for trained models — the boundary between research and production. Covers staging
• Model Serving — Production model serving — low latency, autoscaling, multi-framework, GPU-aware. KServe (CNCF, K8s-native), Be
• Online and Continual Learning — Incremental learning from streaming data, catastrophic forgetting, concept drift detection, and strategies for
• Real-World MLOps Case Studies — Three production case studies covering demand forecasting on Azure, handwriting assistance on GCP, and real-ti
• Reproducible ML Pipelines — Reproducible ML pipelines ensure that a model training run can be exactly re-executed by anyone at any time, p
• Responsible AI and Explainability — Responsible AI and explainability cover the methods and frameworks used to make machine learning models transp

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