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.
Table of contents
What Is Model Composition?When Should I Compose Models?What Are the Benefits of Model Composition?Composing Multiple Models With BentoMLFrequently Asked QuestionsFinal Thoughts8 Impressions