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When ML Meets Microservices: Engineering for Scalability and Performance
- Machine learning models need to be scaled to provide optimal performance as businesses grow and scale
- Microservices are a modern approach to building software that provides flexibility, scalability, and reliability
- Microservices can be combined with machine learning models by splitting each step into its own block
- Using microservices means storing model files, configurations, and other important data in a centralized location
- Keeping parts of the pipeline, such as data preprocessing and feature extraction, separately leads to modularity and enables updating and improving each piece independently
- For real-time instant responses, use lightweight model deployment frameworks and consider using gRPC instead of REST
- For deep learning models, consider using GPUs over CPUs for speeding up predictions
- Kubernetes can be used to deploy, scale, and monitor containers for microservices-based ML systems automatically
- Message brokers like Kafka or RabbitMQ can be used to keep data flowing well between microservices in a microservices setup
- A major challenge in implementing an ML-microservices system is to address the issue of data drift where data is seen in a much different version than the one the model was trained on
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