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# [A Guide to Model Composition](https://api.daily.dev/r/IrYmjLQtO "Go to post")

Model composition is a strategy in machine learning that combines multiple models to solve complex problems. It improves accuracy and performance, allows for dedicated resource allocation, offers customization and flexibility, enables faster development and iteration, and optimizes resource utilization. Some common use cases are multimodal applications, ensemble modeling, and pipeline processing. Model composition requires careful planning and consideration for production deployment.

[#ai](/tags/ai "Check all #ai posts")[#machine-learning](/tags/machine-learning "Check all #machine-learning posts")

May 15, 2024•12m read time•From [thenewstack.io](https://api.daily.dev/r/IrYmjLQtO "thenewstack.io")

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Table of contents

[What Is Model Composition?](https://api.daily.dev/r/IrYmjLQtO "What Is Model Composition?")[When Should I Compose Models?](https://api.daily.dev/r/IrYmjLQtO "When Should I Compose Models?")[What Are the Benefits of Model Composition?](https://api.daily.dev/r/IrYmjLQtO "What Are the Benefits of Model Composition?")[Composing Multiple Models With BentoML](https://api.daily.dev/r/IrYmjLQtO "Composing Multiple Models With BentoML")[Frequently Asked Questions](https://api.daily.dev/r/IrYmjLQtO "Frequently Asked Questions")[Final Thoughts](https://api.daily.dev/r/IrYmjLQtO "Final Thoughts")

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