Editing Ai Infrastructure Mlops (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''MLOps''' — Machine Learning Operations; practices and tools for deploying and maintaining ML models in production reliably and efficiently.
* '''ML pipeline''' — An automated sequence of steps: data ingestion → preprocessing → training → evaluation → deployment → monitoring.
* '''Feature store''' — A centralized repository for computing, storing, and serving ML features consistently across training and inference.
* '''Model registry''' — A central catalog tracking model versions, metadata, performance metrics, and deployment status.
* '''Experiment tracking''' — Recording hyperparameters, metrics, artifacts, and code for each training run to enable comparison and reproducibility.
* '''Model serving''' — The infrastructure for running trained models and serving predictions via API or batch processing.
* '''Inference server''' — A system optimized for serving model predictions at high throughput and low latency (Triton, TorchServe, vLLM).
* '''Continuous Training (CT)''' — Automatically retraining models on fresh data to prevent performance degradation.
* '''Data drift''' — A change in the statistical distribution of input data over time, degrading model performance.
* '''Concept drift''' — A change in the relationship between input features and the target variable, requiring model retraining.
* '''CI/CD for ML''' — Automated testing and deployment pipelines adapted for ML workflows.
* '''GPU cluster''' — A collection of GPUs used for distributed model training and inference.
* '''Kubernetes''' — A container orchestration platform used to deploy and scale ML services.
* '''Ray''' — A distributed computing framework for Python ML workloads.
* '''Weights & Biases (W&B)''' — A popular experiment tracking and model management platform.
* '''Kubeflow''' — An ML workflow orchestration platform built on Kubernetes.
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