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Gone are the days when models were simply trained and left collecting dust on a shelf. Today, the real value of machine learning lies in its ability to improve real-world applications and deliver tangible business results.
However, the path from a trained model to production is full of challenges. Deploying models at scale, ensuring seamless integration with existing infrastructure, and maintaining high performance and reliability are just some of the obstacles MLOP engineers face.
Fortunately, today there are many powerful MLOps tools and frameworks available to simplify and streamline the process of deploying a model. In this blog post, we will learn about the top seven model serving and deployment tools in 2024 that are revolutionizing the way machine learning (ML) models are deployed and consumed.
ml flow is an open source platform that simplifies the entire machine learning lifecycle, including deployment. Provides a Python, R, Java, and REST API to deploy models to various environments, such as AWS SageMaker, Azure ML, and Kubernetes.
MLflow provides a comprehensive solution for managing machine learning projects with features such as model versioning, experiment tracking, reproducibility, model packaging, and model serving.
Lightning serve is a scalable model serving library built on 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 machine learning frameworks and provides features such as response streaming, dynamic request batching, multi-node/GPU serving, version control, and rollbacks.
Kubeflow is an open source framework for deploying and managing machine learning workflows in Kubernetes. It provides a set of tools and components that simplify deployment, scaling, and management of ML models. Kubeflow integrates with popular machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn, and offers features such as 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 run locally on a laptop and in Kubernetes. It provides a flexible and extensible framework for serving models built with various machine learning frameworks.
Seldon Core can be deployed locally using Docker for testing and then scaled into 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 multiple cloud providers.
BentoML is an open source framework that simplifies the process of creating, deploying, and managing machine learning models. It provides a high-level API to package your models in a standardized format called “bentos” and supports multiple deployment options, including AWS Lambda, Docker, and Kubernetes.
BentoML's flexibility, performance optimization, and support for multiple deployment options make it a valuable tool for teams looking to build reliable, scalable, and cost-effective ai applications.
ai/docs/get-started/with-python.html” rel=”noopener” target=”_blank”>ONNX Runtime is an open source cross-platform inference engine for implementing models in the Open Neural Network Exchange (ONNX) format. Provides high-performance inference capabilities across multiple platforms and devices, including CPUs, GPUs, and ai accelerators.
ONNX Runtime supports a wide range of machine learning frameworks such as PyTorch, TensorFlow/Keras, TFLite, scikit-learn and other frameworks. Offers optimizations to improve performance and efficiency.
TensorFlow service is an open source tool for serving TensorFlow models in production. It is designed for machine learning professionals who are familiar with the TensorFlow framework for model tracking and training. The tool is very flexible and scalable, allowing models such as gRPC or REST APIs to be implemented.
TensorFlow Serving has several features, such as model versioning, automatic model loading, and batch processing, that improve performance. It integrates seamlessly with the TensorFlow ecosystem and can be deployed on various platforms such as Kubernetes and Docker.
The tools mentioned above offer a variety of capabilities and can meet 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 your models are easily accessible and scalable. in production.
Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a master's degree in technology management and a bachelor's degree in telecommunications engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.
