MLOps integrates machine learning development with operational processes to ensure AI models are effectively built, deployed, and maintained at scale in production environments.
Building scalable pipelines is essential for AI development, addressing challenges such as handling large datasets, automating model deployment, and monitoring performance drift.
The MLOps lifecycle includes data ingestion, preparation, model development, training, evaluation, deployment, and monitoring, each phase presenting unique challenges that must be addressed.
A major benefit of MLOps is its emphasis on version control and reproducibility, enabling teams to track changes and ensure consistent model performance.
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