Overview

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Deploying machine learning models into production systems requires more than training a model—it requires reliable deployment, monitoring, and debugging practices. In this course, you'll learn how to deploy machine learning models as scalable services and maintain them within real software architectures. You’ll begin by learning how to package and deploy machine learning models using containerization and orchestration technologies. You’ll apply tools such as Docker and Kubernetes to manage application deployment and ensure that models run consistently across environments. Next, you’ll design machine learning services that integrate into distributed system architectures. You’ll explore microservice design patterns, implement REST-based inference services, and analyze communication patterns that support scalable system behavior. You’ll also learn how to monitor deployed ML systems using logs, metrics, and tracing tools that reveal performance issues and system bottlenecks. Finally, you’ll apply debugging and testing techniques to diagnose and resolve problems in machine learning code and infrastructure. Through a hands-on project, you'll deploy and troubleshoot a machine learning microservice, ensuring it performs reliably under real-world conditions.

Syllabus

Deploy, Manage, and Orchestrate Your Models: Containerize and Orchestrate Applications

You will apply containerization and orchestration to deploy and manage applications.

Deploy & Optimize ML Services Confidently: Build and Automate Your ML Inference Service

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

Deploy & Optimize ML Services Confidently: Evaluate and Optimize for SLA Performance

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

Integrate, Scale, and Monitor ML Microservices: Integrate ML Microservices into System Architecture

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

Integrate, Scale, and Monitor ML Microservices: Scale ML Microservices with Asynchronous Messaging

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

Integrate, Scale, and Monitor ML Microservices: Monitor and Maintain ML Microservices with Observability

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

Debug ML Code: Fix, Trace & Evaluate: Test to Isolate: Using Unit Tests to Catch ML Defects Early

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

Debug ML Code: Fix, Trace & Evaluate: Trace the Failure: Using Logs and Stack Traces to Find Root Causes

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

Debug ML Code: Fix, Trace & Evaluate: Validate the Fix: Regression Testing and Confirming Defect Resolution

You will evaluate corrective actions to confirm defect resolution.

Project: Deploy, Scale, Monitor & Debug an ML Microservice

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.