Top Frameworks for Model Serving in 2023

November 1, 2023

If you are a beginner to data science, you would have developed and trained Machine Learning models like regressors in your Jupyter Notebook environment. Check out the services offered by E2E Cloud. When you want to test it, you pass your input and generate the inference by calling the model in another notebook. 

Ever wondered how companies deploy their ML models and generate results in real time? That’s where model serving comes in!

Model serving is the process of deploying machine learning models to production environments, making them accessible for real-time predictions and decision-making. Under the model serving umbrella, various frameworks and tools are available for businesses to choose from. In this article, I’ll walk you through the top model serving frameworks of 2023, along with their unique features, advantages and shortcomings.

Why Choosing the Right Model Serving Framework Matters

The choice of model serving framework can have a significant impact on many aspects of your ML application, including performance. Let’s take a quick look at the impact of model serving on end result:

  • Latency: Would you talk to a chatbot that takes 10 minutes to respond to a text? Never. Having low latency or shorter response time is crucial for real-time ML models. You need to keep in mind user satisfaction while choosing your framework.
  • Scalability: Let’s say you have a chatbot on a women’s clothing website. There would be a sudden increase in traffic during festive times like Christmas. Your model should be able to handle the heavy demand without breaking down, and that depends on the framework and infrastructure you choose. Your framework should support up-scaling, handling increasing input volumes without compromising on response time.
  • Security: Model serving frameworks can help to protect your model from security threats. For example, some frameworks provide features such as model encryption and authentication.
  • Monitoring and Logging: Robust monitoring and logging capabilities help you track model performance easily and detect any alerts. Version management also falls under this.

Now, let's delve into some of the top model serving frameworks of 2023 and what they bring to the table.

Top Model Serving Frameworks

  1. TorchServe: TorchServe is a model-serving framework that is designed for PyTorch models. Its features include dynamic batching, multi-model serving, and model management features. The main advantage is that it offers seamless integration with PyTorch (which is widely used in deep learning) and supports dynamic model loading.
  1. Kubeflow Serving: It is built on Kubernetes and is a scalable, portable, and multi-framework model serving a solution that supports TensorFlow, PyTorch, etc. It can support a wide range of ML packages. But you would need a technical expert in Kubernetes for set-up and maintenance.
  1. MLflow: It provides a simple REST API for serving models. It provides seamless integration with the MLflow platform (a widely used end-to-end ML applications platform). It has features that allow built-in experimentation and tracking.
  1. Triton Inference Server: It is an open-source model serving platform from NVIDIA with support for a variety of deep learning frameworks. It has shown high performance for GPU-accelerated models and supports various machine learning frameworks. If you have heavy LLMs or DL models, you know where to go!
  1. Model Serving: It is a serving solution provided by the company. It is designed for their machine learning models and AutoML products. It works best and is optimized for models, offering seamless integration. But it may not be robust to models built on other packages.
  1. Azure Machine Learning: Microsoft's Azure Machine Learning service includes model serving features and integration with Azure Kubernetes Service (AKS) for deploying models. It provides easy integration with Azure cloud services and offers a unified environment for model development and tracking.
  1. TensorFlow Serving: Highly versatile and designed specifically for serving TensorFlow models. The top features include model versioning, RESTful APIs, and efficient request handling. As a cherry on top, there is strong community support to help individual developers and open-source developers. But it has limited support in a heterogeneous AI ecosystem.

  1. KFServing: A Kubernetes-native serverless framework for serving machine learning models with model versioning and multi-model serving capabilities. It offers great flexibility and serverless deployment, reducing operational overheads. Small-scale start-ups and individuals may prefer this.
  1. Clipper: It is an open-source model-serving framework that supports multiple machine learning libraries. It’s known for providing low-latency predictions. Other benefits include high flexibility, compatibility with various libraries, and a robust monitoring system.
  1. Seldon: Seldon offers extensive support for model deployment on Kubernetes, with robust model-serving features and monitoring capabilities. It is versatile and agnostic to machine learning frameworks, making it suitable for diverse AI ecosystems.
  1. Amazon SageMaker Model Serving: This framework provides fully managed model serving, support for a wide range of models, and is easy to use. It provides security and scalability during times of high demand.
  1. BentoML: The features of this platform include eModel serving, model packaging, and model deployment. It is also easy to use, portable between environments, and supports a wide range of models.
  1. Pachyderm: The best features include easy-to-maintain data versioning, model serving, and experiment tracking. Using this, you can easily reproduce or port your ML models.
  1. Vertex AI: It is an ML platform that provides an integrated and comprehensive environment for developing, deploying, and managing machine learning models. It offers autoML for quick model development. It has Google Cloud's infrastructure which ensures scalability and low-latency model serving. But the cost may be higher than other options.
  1. Determined AI: The best features are distributed training, model serving, and experiment tracking. It is scalable, performs well and is easy to use, even though it is not very popular.
  1. Cortex: It is an open-source model serving platform focusing on ease of use and scalability. It supports a variety of machine learning frameworks and offers features like autoscaling, multi-model serving, and real-time inference pipelines. It has a user-friendly interface and supports diverse ML libraries including TensorFlow, PyTorch, and sci-kit-learn.
  1. Jina: Jina is an open-source neural search framework designed for building powerful search and recommendation systems. It supports multiple neural network frameworks and offers capabilities for distributed and parallel computing. It's well-suited for search, recommendation, and large-scale text and image processing.
  1. Langchain Serve: It is an open-source model serving framework that specializes in serving LLMs. Using this, you can easily deploy pre-trained language models for tasks like language generation, translation, and sentiment analysis. It includes features like model versioning and RESTful API support. It is helpful in reducing the overhead of setting up serving infrastructure. 

Every coin has two sides. Similarly, each of these frameworks caters to various machine learning libraries, deployment needs, and environments. As a business, you need to choose the one that best fits your requirements and infrastructure.

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Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

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  • It can also help in completing DNA sequences.

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