MLOps, Machine Learning Operations, is the process of machine learning model operationalisation and combines software engineering and machine learning to ensure that models are deployed, monitored, and maintained effectively.
MLOps uses automation tools and processes to achieve faster time-to-market, improve model performance and reduce the risk of model failure. MLOps improves model performance by providing real-time feedback and insights. Tools include Kubeflow, TensorFlow, and MLflow. Cloud-based platforms such as AWS, GCP, and Azure are being used to deploy and scale machine learning models.
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Source: Ashish Patel on Linkedin