This course is part of MLOps | Machine Learning Operations Specialization
Data for MLOps Course will be AVAILABLE SOON.
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What you’ll learn
✓ Understand how data flows through ML systems and how to build pipelines that are simple, clear, and repeatable.
✓ Learn to clean, check, and track data to make sure your models work well and do not fail silently in production.
✓ Use tools to version datasets, store features, and test data quality.
✓ Monitor how data changes over time, detect drift, and set up alerts so you know when retraining is needed.
There are 11 modules in this course
- Introduction
- Introduction to Data
- Data Ingestion & Integration
- Data Validation & Quality Assurance
- Feature Engineering
- Dataset Versioning & Reproducibility
- Data Lineage, Metadata, and Governance
- Data Drift, Concept Drift, and Monitoring
- Data Pipeline Design for ML
- Use Cases
- Capstone Project
About this course
In this course, you will learn why data is a core part of Machine Learning Operations (MLOps) and how it should be managed to build stable, traceable, and production-ready ML systems. In many real-world projects, the data changes often, even when the code does not, and this is one of the main reasons why models break in production. MLOps helps solve this problem by treating data with the same discipline as code.
We will explore how data flows through the ML lifecycle – from ingestion and validation to transformation, versioning, and monitoring. You will see what MLOps Engineers do to make data pipelines reliable, repeatable, and testable, so that model training and deployment can happen with confidence. We will also cover how to detect when data changes over time and what actions can be taken to keep your systems accurate and up to date.
By the end of the course, you will understand how to design and manage data pipelines that support the entire ML workflow – not just once, but every day. You will also learn the key principles of data governance, quality assurance, and reproducibility that help teams avoid silent failures and improve collaboration across roles.
The goal of this course is simple: to give you a clear understanding of how data should be handled in MLOps. If you want to work with ML in real environments, not just in notebooks, this course will help you build the right habits from the start.
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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