HomeData scienceHigh 7 Mannequin Deployment and Serving Instruments

High 7 Mannequin Deployment and Serving Instruments

Top 7 Model Deployment and Serving Tools
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Gone are the times when fashions had been merely skilled and left to gather mud on a shelf. At this time, the actual worth of machine studying lies in its capability to boost real-world functions and ship tangible enterprise outcomes.

Nevertheless, the journey from a skilled mannequin to a manufacturing is stuffed with challenges. Deploying fashions at scale, guaranteeing seamless integration with present infrastructure, and sustaining excessive efficiency and reliability are only a few of the hurdles that MLOPs engineers face.

Fortunately, there are a lot of highly effective MLOps instruments and frameworks accessible these days to simplify and streamline the method of deploying a mannequin. On this weblog publish, we are going to be taught concerning the prime 7 mannequin deployment and serving instruments in 2024 which might be revolutionizing the best way machine studying (ML) fashions are deployed and consumed.



MLflow is an open-source platform that simplifies the complete machine studying lifecycle, together with deployment. It gives a Python, R, Java, and REST API for deploying fashions throughout numerous environments, resembling AWS SageMaker, Azure ML, and Kubernetes. 

MLflow gives a complete answer for managing ML initiatives with options resembling mannequin versioning, experiment monitoring, reproducibility, mannequin packaging, and mannequin serving. 



Ray Serve is a scalable mannequin serving library constructed on prime of the Ray distributed computing framework. It permits you to deploy your fashions as microservices and handles the underlying infrastructure, making it simple to scale and replace your fashions. Ray Serve helps a variety of ML frameworks and gives options like response streaming, dynamic request batching, multi-node/multi-GPU serving, versioning, and rollbacks.



Kubeflow is an open-source framework for deploying and managing machine studying workflows on Kubernetes. It gives a set of instruments and elements that simplify the deployment, scaling, and administration of ML fashions. Kubeflow integrates with widespread ML frameworks like TensorFlow, PyTorch, and scikit-learn, and gives options like mannequin coaching and serving, experiment monitoring, ml orchestration, AutoML, and hyperparameter tuning.



Seldon Core is an open-source platform for deploying machine studying fashions that may be run regionally on a laptop computer in addition to on Kubernetes. It gives a versatile and extensible framework for serving fashions constructed with numerous ML frameworks.

Seldon Core will be deployed regionally utilizing Docker for testing after which scaled on Kubernetes for manufacturing. It permits customers to deploy single fashions or multi-step pipelines and may save infrastructure prices. It’s designed to be light-weight, scalable, and appropriate with numerous cloud suppliers.



BentoML is an open-source framework that simplifies the method of constructing, deploying, and managing machine studying fashions. It gives a high-level API for packaging your fashions into standardized format referred to as “bentos” and helps a number of deployment choices, together with AWS Lambda, Docker, and Kubernetes. 

BentoML’s flexibility, efficiency optimization, and assist for numerous deployment choices make it a precious software for groups seeking to construct dependable, scalable, and cost-efficient AI functions.



ONNX Runtime is an open-source cross-platform inference engine for deploying fashions within the Open Neural Community Alternate (ONNX) format. It gives high-performance inference capabilities throughout numerous platforms and units, together with CPUs, GPUs, and AI accelerators. 

ONNX Runtime helps a variety of ML frameworks like PyTorch, TensorFlow/Keras, TFLite, scikit-learn, and different frameworks. It gives optimizations for improved efficiency and effectivity.



TensorFlow Serving is an open-source software for serving TensorFlow fashions in manufacturing. It’s designed for machine studying practitioners who’re accustomed to the TensorFlow framework for mannequin monitoring and coaching. The software is very versatile and scalable, permitting fashions to be deployed as gRPC or REST APIs. 

TensorFlow Serving has a number of options, resembling mannequin versioning, automated mannequin loading, and batching, which improve efficiency. It seamlessly integrates with the TensorFlow ecosystem and will be deployed on numerous platforms, resembling Kubernetes and Docker.



The instruments talked about above supply a spread of capabilities and may cater to totally different wants. Whether or not you like an end-to-end software like MLflow or Kubeflow, or a extra centered answer like BentoML or ONNX Runtime, these instruments might help you streamline your mannequin deployment course of and be certain that your fashions are simply accessible and scalable in manufacturing.

Abid Ali Awan (@1abidaliawan) is a licensed information scientist skilled who loves constructing machine studying fashions. Presently, he’s specializing in content material creation and writing technical blogs on machine studying and information science applied sciences. Abid holds a Grasp’s diploma in expertise administration and a bachelor’s diploma in telecommunication engineering. His imaginative and prescient is to construct an AI product utilizing a graph neural community for college students scuffling with psychological sickness.

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