Top 7 Model Deployment and Serving Tools

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Gone are the days when models were simply trained and left to collect dust on a shelf. Today, the real value of machine learning lies in its ability to enhance real-world applications and deliver tangible business outcomes.

However, the journey from a trained model to a production is filled with challenges. Deploying models at scale, ensuring seamless integration with existing infrastructure, and maintaining high performance and reliability are just a few of the hurdles that MLOPs engineers face.

Thankfully, there are many powerful MLOps tools and frameworks available nowadays to simplify and streamline the process of deploying a model. In this blog post, we will learn about the top 7 model deployment and serving tools in 2024 that are revolutionizing the way machine learning (ML) models are deployed and consumed.

 

 

MLflow is an open-source platform that simplifies the entire machine learning lifecycle, including deployment. It provides a Python, R, Java, and REST API for deploying models across various environments, such as AWS SageMaker, Azure ML, and Kubernetes. 

MLflow provides a comprehensive solution for managing ML projects with features such as model versioning, experiment tracking, reproducibility, model packaging, and model serving. 

 

 

Ray Serve is a scalable model serving library built on top of the Ray distributed computing framework. It allows you to deploy your models as microservices and handles the underlying infrastructure, making it easy to scale and update your models. Ray Serve supports a wide range of ML frameworks and provides features 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 learning workflows on Kubernetes. It provides a set of tools and components that simplify the deployment, scaling, and management of ML models. Kubeflow integrates with popular ML frameworks like TensorFlow, PyTorch, and scikit-learn, and offers features like model training and serving, experiment tracking, ml orchestration, AutoML, and hyperparameter tuning.

 

 

Seldon Core is an open-source platform for deploying machine learning models that can be run locally on a laptop as well as on Kubernetes. It provides a flexible and extensible framework for serving models built with various ML frameworks.

Seldon Core can be deployed locally using Docker for testing and then scaled on Kubernetes for production. It allows users to deploy single models or multi-step pipelines and can save infrastructure costs. It is designed to be lightweight, scalable, and compatible with various cloud providers.

 

 

BentoML is an open-source framework that simplifies the process of building, deploying, and managing machine learning models. It provides a high-level API for packaging your models into standardized format called “bentos” and supports multiple deployment options, including AWS Lambda, Docker, and Kubernetes. 

BentoML’s flexibility, performance optimization, and support for various deployment options make it a valuable tool for teams looking to build reliable, scalable, and cost-efficient AI applications.

 

 

ONNX Runtime is an open-source cross-platform inference engine for deploying models in the Open Neural Network Exchange (ONNX) format. It provides high-performance inference capabilities across various platforms and devices, including CPUs, GPUs, and AI accelerators. 

ONNX Runtime supports a wide range of ML frameworks like PyTorch, TensorFlow/Keras, TFLite, scikit-learn, and other frameworks. It offers optimizations for improved performance and efficiency.

 

 

TensorFlow Serving is an open-source tool for serving TensorFlow models in production. It is designed for machine learning practitioners who are familiar with the TensorFlow framework for model tracking and training. The tool is highly flexible and scalable, allowing models to be deployed as gRPC or REST APIs. 

TensorFlow Serving has several features, such as model versioning, automatic model loading, and batching, which enhance performance. It seamlessly integrates with the TensorFlow ecosystem and can be deployed on various platforms, such as Kubernetes and Docker.

 

 

The tools mentioned above offer a range of capabilities and can cater to different needs. Whether you prefer an end-to-end tool like MLflow or Kubeflow, or a more focused solution like BentoML or ONNX Runtime, these tools can help you streamline your model deployment process and ensure that your models are easily accessible and scalable in production.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.