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Just as DevOps brought structure and automation to traditional software engineering, MLOps brings the same rigor to machine learning. It encompasses the entire lifecycle from data gathering, model training, and versioning, to deployment, monitoring, and scaling. Without MLOps, models often remain stuck in research environments or degrade significantly when exposed to real-world data.
Machine Learning Operations (MLOps) is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently.
We help teams go from definitions to deployed workflows—safely and fast.
Unlike traditional software code which is relatively static, machine learning models degrade over time as the real world changes (data drift). MLOps provides the CI/CD pipelines needed to automatically retrain, evaluate, and deploy new models before performance drops to unacceptable levels.
With the rise of large language models, MLOps has evolved into LLMOps. This new paradigm focuses less on training from scratch and more on prompt management, fine-tuning, RAG pipeline orchestration, and handling LLM-specific evaluations like hallucination grading.
DevOps deals primarily with code. MLOps deals with code, data, and models. Testing code is straightforward (it passes or fails), but testing a model requires statistical evaluation on holdout datasets.
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