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Artificial Intelligence
MLOps
MLOps is the practice of deploying, monitoring, and maintaining ML models in production.
Definition
MLOps (Machine Learning Operations) is a set of practices that combines machine learning, DevOps, and data engineering to deploy, monitor, and maintain ML models in production. Key MLOps components: (1) Model registry — versioning and storing models, (2) CI/CD pipelines — automated training and deployment, (3) Monitoring — tracking model performance, drift, fairness, (4) Feature store — managing ML features, (5) Experiment tracking — logging training runs. Harch Corp provides GPU infrastructure compatible with popular MLOps tools — MLflow, Kubeflow, Weights & Biases.
Related Keywords
mlopsml operationsmodel deploymentml pipelineai infrastructure
Related Terms
AI Training
AI training is the process of optimizing model parameters using labeled data and compute resources.
AI Inference
AI inference is the process of using a trained model to make predictions on new data.
LLM Serving
LLM serving is the deployment of large language models for inference via APIs or applications.