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Deploying and Debugging ML Microservices

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Deploying and Debugging ML Microservices

This course is part of Machine Learning Made Easy for Software Engineers Specialization

Instructor: Professionals from the Industry

Included with

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10 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

10 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Deploy machine learning models using containerization and orchestration tools such as Docker and Kubernetes
Design scalable ML inference services using microservice architecture principles
Monitor and debug ML systems using logs, testing techniques, and performance analysis

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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Build your subject-matter expertise

This course is part of the Machine Learning Made Easy for Software Engineers Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 10 modules in this course

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.

Module details

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

What's included

3 videos2 readings2 assignments

3 videosTotal 10 minutes

Introduction and Welcome2 minutes
Writing a Dockerfile for Your Model3 minutes
Deploying Containers in Kubernetes4 minutes

2 readingsTotal 18 minutes

Publishing to an Internal Registry8 minutes
Managing and Monitoring Containers10 minutes

2 assignmentsTotal 55 minutes

Hands-On Activity: Build, Deploy, and Test Your Model25 minutes
Graded Quiz: Deploy and Orchestrate ML Models30 minutes

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

What's included

3 videos1 reading3 assignments

3 videosTotal 11 minutes

Welcome and Course Overview3 minutes
From Model to Service — The RESTful Inference Journey 5 minutes
Continuous Integration — Testing for Confidence 3 minutes

1 readingTotal 6 minutes

Deploying Scikit-Learn Models as REST APIs with Fast API: A Developer’s Guide6 minutes

3 assignmentsTotal 51 minutes

Hands-On Activity: Build Your Inference API25 minutes
Hands-On Activity: Automate, Build and Deploy with GitHub Actions 20 minutes
Practice Quiz: From Notebook to Production6 minutes

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

What's included

3 videos2 readings2 assignments

3 videosTotal 16 minutes

What Does “Good Performance” Really Mean?5 minutes
Measuring Latency — Tools, Process, and Why It Matters6 minutes
Optimize with Confidence — Scaling and Container Tweaks6 minutes

2 readingsTotal 11 minutes

P50 vs P95 vs P99 Latency: What These Percentiles Actually Mean (And How to Use Them)5 minutes
How P90, P95, and P99 Shape System Performance6 minutes

2 assignmentsTotal 50 minutes

Hands-On Activity: Load Test, Optimize, and Validate Your ML Service30 minutes
Graded Quiz: Inference Service Confidence Challenge 20 minutes

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

What's included

3 videos1 reading1 assignment

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

What's included

2 videos1 reading2 assignments

2 videosTotal 11 minutes

Scaling ML Systems with Asynchronous Messaging5 minutes
Building a Prediction Queue: Real-World Patterns6 minutes

1 readingTotal 6 minutes

Kafka Data Pipelines: Best Practices for High-Throughput Streaming6 minutes

2 assignmentsTotal 30 minutes

Hands-On Activity: Build a Kafka Prediction Pipeline25 minutes
Practice Quiz: Assessing Async Patterns, Partitioning Choices, and Throughput Reasoning5 minutes

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

What's included

1 video1 reading2 assignments

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

What's included

2 videos1 reading1 assignment

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

What's included

1 video1 reading1 assignment

You will evaluate corrective actions to confirm defect resolution.

What's included

1 video1 reading2 assignments

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.