How I Split My ML App into Microservices (Because a Monolithic Dockerfile Was a Bad Idea)
📰 Medium · Data Science
Learn how to split a Machine Learning app into microservices to improve scalability and maintainability
Action Steps
- Identify separate components of your ML app that can be split into microservices
- Design a service-oriented architecture for your ML app
- Create separate Dockerfiles for each microservice
- Implement communication between microservices using APIs or message queues
- Test and deploy each microservice independently
Who Needs to Know This
Data scientists and software engineers working on ML projects can benefit from this approach to improve collaboration and reduce technical debt
Key Insight
💡 Monolithic architectures can hinder ML app development, while microservices enable flexibility and collaboration
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Split your ML app into microservices for better scalability and maintainability!
Key Takeaways
Learn how to split a Machine Learning app into microservices to improve scalability and maintainability
Full Article
When developing Machine Learning applications, the initial focus is almost always on training the model and looking at validation scores… Continue reading on Medium »
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