From Models to Microservices: Deploying ML in Java and .NET

Introduction

Deploying Machine Learning models in enterprise environments can be challenging. Moving from a notebook to a scalable service requires more than accuracy: you need integration, maintainability, and automation.

Why ML Deployment Is Hard

The gap between research and production

Models work in notebooks but fail on servers. Dependencies, resources, and data evolve.

Common pitfalls

• No versioning for models.
• Manual or unmonitored scaling.
• Lack of automated tests.

Deploying ML with Java

Using Spring Boot for model serving

Spring Boot allows packaging an ONNX or TensorFlow model as a REST microservice.

Integration with ONNX and TensorFlow Java

You can load the model directly and expose it via endpoints.

Deploying ML with .NET

Using ML.NET and API endpoints

ML.NET enables training and serving models directly in C#.

Cross-language interoperability

ONNX allows running Python-trained models inside .NET applications.

CI/CD for ML Microservices

Model versioning

Use version control (Git or DVC) to track model and data changes.

Automation

Integrate deployment pipelines with GitHub Actions or Azure DevOps.

Final Checklist

• [ ] Is the model versioned and tested?
• [ ] Do the REST endpoints work with real data?
• [ ] Does deployment include monitoring?
• [ ] Is model update automated through CI/CD?

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Tags: ml, java, dotnet, microservices