8 Essential ML Model Deployment Tools for Data Scientists in 2025

Deploying machine learning (ML) models in production requires reliable tools that ensure scalability, monitoring, and easy management. This article reviews eight popular ML model deployment solutions that will help you effectively organize the MLOps process.

1. Seldon

Seldon Core is an open-source framework for deploying models in Kubernetes. It supports various ML frameworks, integrates with CI/CD, and offers model interpretability. Pros:

• Supports offline models and APIs for external clients.

• Automated deployment through CI/CD.

• Flexibility and scalability. Cons:

• Complex setup.

• High learning curve for beginners.

2. BentoML

BentoML provides a standard Python-based architecture for deploying and supporting ML model APIs. It supports online and offline serving and automatically generates Docker images. Pros:

• Fast API deployment.

• High-performance serving.

• Supports multiple platforms. Cons:

• No built-in experiment management.

• No out-of-the-box horizontal scaling.

3. TensorFlow Serving

This tool from Google allows deploying TensorFlow models as REST API endpoints. Pros:

• High performance and batch request processing.

• Model versioning support.

• Easy integration with TensorFlow. Cons:

• Works only with TensorFlow models.

• No built-in zero-downtime model updates.

4. Kubeflow

Kubeflow is a platform for managing ML workflows in Kubernetes. Pros:

• Supports Docker and containerization.

• ML pipeline management.

• High flexibility and scalability. Cons:

• Complex configuration.

• Steep learning curve.

5. Cortex

An open-source tool for deploying models, supporting AWS, Kubernetes, and Lambda. Pros:

• Automatic API scaling.

• Supports multiple ML frameworks.

• Model updates without downtime. Cons:

• Complex setup process.

6. AWS SageMaker

SageMaker from Amazon offers a complete ML model lifecycle—from training to production deployment. Pros:

• Supports Jupyter Notebook.

• Automated scaling.

• Flexible pricing. Cons:

• High entry barrier.

• Rigid workflows.

• Limited to the AWS ecosystem.

7. MLflow

MLflow is an open-source tool for organizing the full ML model lifecycle. Pros:

• Easy experiment management.

• Supports multiple ML frameworks.

• Logging and reproducibility. Cons:

• Requires manual model configuration.

• Limited deployment capabilities.

8. TorchServe

A framework for serving PyTorch models, developed by AWS and PyTorch. Pros:

• High performance.

• Built-in libraries for predictions.

• RESTful API for integration. Cons:

• Frequent updates.

• Works only with PyTorch.

Conclusion

Choosing the right tool depends on your scalability needs, integration preferences, and supported ML frameworks. If you need a Kubernetes-oriented approach, consider Seldon and Kubeflow. For AWS integration, SageMaker and Cortex are great choices, while MLflow and BentoML help with organizing and automating the deployment process.

Which tool do you use? Share your experience in the comments!