MLOps
• Streamlining Deployment and Management: MLOps, or DevOps
for machine learning, incorporates best practices from software
development into the machine learning lifecycle. It aims to simplify
the models' deployment, monitoring, and management, making them
more scalable, reliable, and maintainable.
• Enhancing Collaboration and Efficiency: Implementing MLOps
practices fosters closer collaboration between data scientists, IT
professionals, and operational teams. It also improves efficiency by
automating many aspects of the machine learning workflow, from
data preparation and model training to deployment and monitoring.
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