DeployMint: Serve Models with Python Spice
Model deployment and serving are critical steps in bringing machine learning models to practical use. This category focuses on libraries and tools that facilitate the deployment and serving of machine learning models in production environments. From creating scalable APIs to managing model versions, these tools ensure seamless integration of models into real-world applications.
TensorFlow Serving
TensorFlow Serving is a part of the TensorFlow Extended (TFX) ecosystem and is designed for serving machine learning models in production. It allows for seamless integration with TensorFlow models.
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These libraries and tools empower data scientists and machine learning engineers to deploy and serve models efficiently, ensuring that predictive models can be seamlessly integrated into real-world applications. Explore these options to find the one that best suits your deployment needs.