What is MLOps?
MLOps (Machine Learning Operations) is the set of practices and processes of streamlining and optimizing the deployment, monitoring, and maintenance of ML models in production environments. This ensures that the models are effective, efficient, and scalable, so they can reach production in an efficient, cost-effective and timely manner.
MLOps is a merger of ML with DevOps practices to cover the entire lifecycle of the ML model, from development and testing to deployment and maintenance. Activities include managing data, selecting algorithms, training models, and evaluating their performance. This is done automatically, at scale, and while enhancing collaboration.
LLMOPS vs MLOPS: Making the Right Choice
In the rapidly evolving landscape of artificial intelligence and machine learning, new terminologies and concepts frequently emerge, often causing confusion among business leaders, IT analysts, and decision-makers. While sounding similar, LLMOps and MLOps represent distinct approaches that can significantly impact how organizations harness the power of AI technologies.
LLMOPS vs MLOPS: Making the Right Choice
This article compares LLMOps and MLOps, clarifying their roles, and illustrating the impact of each approach on the deployment and management of AI initiatives.
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