This course is part of MLOps | Machine Learning Operations Specialization
We are actively working on Observability for MLOps Course and it will be AVAILABLE SOON.
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What you’ll learn
✓ Understand key observability tools and ideas to watch how ML systems work in real time.
✓ Set up dashboards and alerts to follow your models, data, and infrastructure in production.
✓ Learn how to catch problems like data drift, broken pipelines, or failed predictions early.
✓ Use tools like Prometheus, Grafana, and MLflow to build a full view of your ML pipeline health.
There are 10 modules in this course
- Introduction
- Metrics and Monitoring
- Logging in MLOps
- Tracing and Debugging ML Pipelines
- Model Monitoring and Drift Detection
- Data Quality Monitoring
- Alerting and Incident Response
- Observability Patterns & Best Practices
- Tool Integrations
- Capstone Project
About this course
In this course, you will learn how observability helps Machine Learning systems run smoothly in production. Machine Learning models are not just built once, they need to be checked, tracked, and supported every day. Observability makes this possible by helping teams see what is happening inside their systems, so they can find problems early and keep things working well.
We will explore the key ideas behind observability, including how to follow your data, watch your models, and understand how different parts of your ML pipeline are doing. You will learn how to track both the system side (like memory or speed) and the ML side (like prediction quality or data changes). You will also understand how to respond when something goes wrong, like a drop in accuracy or a broken step in your pipeline.
This course is focused on the daily work of MLOps Engineers. It does not teach how to build models, instead, it shows how to support them in real life. You will learn what to watch, what to measure, and how to act fast when needed.
By the end of the course, you will have a clear view of how observability fits into MLOps. You will understand how it helps teams manage risk, keep systems stable, and make better decisions. This course is a key step if you want to work in MLOps and build reliable ML systems at scale.
Start learning high demand tech skills today
Hi, I’m Alex and I’ve spent over 20 years helping well known startups and enterprises introduce innovations. I also developed and taught Cloud&DevOps part for a Master’s Degree at the University.
In this course, I’ll show you what MLOps looks like in practice – step by step, with real tools and clear guidance.
You don’t need to be an expert. If you want to understand how to start or enforce your career as MLOps Engineer, not just in theory, but in real life, this course is for you. Let’s get started.
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