MLOps, or DevOps for machine learning, is the practice of combining software development and operations in the context of machine learning. It involves the use of tools and processes to build, test, deploy, and manage machine learning models in a production environment.
MLOps focuses on improving the collaboration, communication, and automation of the machine learning lifecycle. This includes automating the building and training of machine learning models, as well as the deployment and management of those models in a production environment.
MLOps also emphasizes the importance of monitoring and managing the performance of machine learning models in production. This can include tracking model accuracy and performance over time, as well as identifying and addressing any potential issues or failures.
Overall, the goal of MLOps is to enable organizations to more effectively and efficiently develop, deploy, and manage machine learning models in a production environment. This can help organizations accelerate their use of machine learning and realize the full potential of their data and algorithms.
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