How I Split My ML App into Microservices (Because a Monolithic Dockerfile Was a Bad Idea)
📰 Medium · Machine Learning
Learn how to split a Machine Learning app into microservices to improve scalability and maintainability
Action Steps
- Identify the components of your ML app that can be separated into microservices
- Create a separate Dockerfile for each microservice
- Use a container orchestration tool like Kubernetes to manage and deploy the microservices
- Configure communication between microservices using APIs or message queues
- Test and monitor the microservices to ensure they are working together seamlessly
Who Needs to Know This
Machine Learning engineers and developers can benefit from this approach to improve the organization and scalability of their ML applications
Key Insight
💡 Breaking down a monolithic ML app into microservices can improve scalability, maintainability, and reduce deployment risks
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💡 Split your ML app into microservices to improve 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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