MLOps (Machine Learning Operations) applies DevOps principles to the machine learning lifecycle, bridging the gap between experimental data science models and reliable 24/7 production systems. Encompassing data versioning, automated model retraining, containerized inference serving, and data drift monitoring, MLOps keeps models accurate over time.
Shish Technology designs automated MLOps pipelines using MLflow, Docker, Triton Inference Server, and Kubernetes.
