What is MLOps (Machine Learning Operations)?
MLOps is the set of practices, tools, and cultural changes needed to deploy, monitor, and maintain machine learning models in production reliably.
MLOps is the set of practices, tools, and cultural changes needed to deploy, monitor, and maintain machine learning models in production reliably. It applies DevOps principles to the ML lifecycle: data management, model training, deployment, monitoring, and retraining.
MLOps addresses the unique challenges of ML in production: model drift (accuracy degrades as real-world data changes), data pipeline failures, reproducibility requirements, A/B testing for model versions, and cost management for GPU-intensive workloads.
Key MLOps tools include: MLflow and Weights & Biases (experiment tracking), Kubeflow and SageMaker (training orchestration), Seldon and BentoML (model serving), Great Expectations (data quality), and Evidently AI (model monitoring).
In 2026, MLOps has expanded to include LLMOps — the specific practices for managing large language model applications, including prompt versioning, RAG pipeline management, hallucination monitoring, and inference cost optimization.
Why It Matters
Most ML projects fail in production, not in development. MLOps practices determine whether your AI investment generates returns or becomes an expensive prototype that never scales beyond a demo environment.
Frequently Asked Questions
What is MLOps?
MLOps applies DevOps practices to machine learning: automated training pipelines, model deployment, monitoring, and retraining. It ensures ML models work reliably in production.
What is the difference between MLOps and LLMOps?
MLOps covers traditional ML models (classification, regression). LLMOps covers LLM-specific concerns: prompt management, RAG pipelines, hallucination monitoring, and inference cost optimization.
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