3 Min Guide: Real-Time Model Inference with Kubeflow & Kubernetes

Facing an interview or building an ML inferencing platform? You'll need a solid grasp on Kubeflow and Kubernetes. Here, we unpack their architecture for real-time transactional model inferencing. Dive in.

Here is one approach to deploying real-time transactional model inferencing using Kubeflow, Kubernetes, and machine learning:

🎯 Use Kubeflow Pipelines for training and versioning machine learning models, outputting to a model repository like S3.

🎯 Containerize model inference code using Docker and Kubernetes. Key practices include separating code and config.

🎯 Deploy trained models from the repository to a Kubernetes cluster for portable, container-based execution.

🎯 Use Kubernetes for autoscaling inferencing workloads - elasticity based on load.

🎯 Expose inferencing via Kubernetes Service or Istio Gateway for consistent endpoints.

🎯 Handle user transactions and requests using a microservices architecture on Kubernetes, enabling independent scaling.

🎯 Accept user requests, route to the inference service, and return predictions with the Choreography pattern.

🎯 Keep inferencing service highly available with Kubernetes liveness and readiness probes, plus health checks.

🎯 Use Kubernetes Jobs or CronJobs for retraining models on new data, updating the inference service.

🎯 Instrument everything using Prometheus for monitoring and alerts. Establish and meet SLAs.

🎯 Enable tracing with tools like Jaeger to find bottlenecks and latency.

🎯 Implement real-time logging for auditing and debuggability.

🎯 Use Kubernetes namespaces and networking policies for multi-tenancy and isolation.

The core ideas are to build for scalability, availability, and deployability using Kubernetes and ML best practices. With the right architecture, we can deliver low-latency, robust inferencing for real-time user transactions.