You will create a RESTful inference service and integrate it into a CI/CD pipeline.

You will evaluate a deployed service's performance metrics against SLA targets.

You will apply microservice design principles to integrate an ML inference service into a system architecture.

You will analyze inter-service communication patterns to implement asynchronous messaging for scalability.

You will evaluate system observability using logs, metrics, and distributed tracing to maintain system health and performance.

You will apply software testing techniques to isolate defects in machine learning code.

You will analyze stack traces and logs to identify the root cause of system failures.

You will evaluate corrective actions to confirm defect resolution.

In this project, you will design and implement a containerized machine learning microservice system that delivers model predictions through a scalable inference API. A financial services platform uses a machine learning model to estimate credit risk for loan applications, and the engineering team must deploy it as a reliable production service capable of handling thousands of requests per hour. Your task is to build a simplified ML inference microservice architecture that includes a Python-based inference API, Docker containerization, Kubernetes deployment configuration, a RESTful inference service with CI/CD pipeline integration, inter-service communication patterns for asynchronous messaging, observability using structured logs, metrics, and distributed tracing, performance monitoring using service-level metrics, debugging analysis of simulated runtime failures, and a regression testing strategy. The final deliverable is a modular inference microservice script and deployment configuration, along with a structured engineering explanation describing deployment, communication, observability, and debugging decisions.