MLOps Fundamentals Course | MLOps Specialization

A practical introduction to modern MLOps: ML model lifecycle, CI/CD, infrastructure, observability, and core tools.

What you'll learn
- Understand the foundational concepts of MLOps, including its differences from DevOps and why it matters in real-world ML projects.
- Identify key components of the ML model lifecycle, such as training, validation, deployment, and monitoring.
- Gain practical skills to work with CI/CD pipelines, containers, and cloud infrastructure for ML projects.
- Recognize the importance of observability, monitoring, data quality, and reproducibility in production ML environments.

Requirements
- This course is designed for a broad technical audience and has no strict prerequisites.
- Basic familiarity with Python (optional)
- A general understanding of software development concepts (optional)
- Interest in learning how machine learning is used in production environments

Description
Are you ready to unlock the essential skills to support machine learning in real-world production environments?

In today’s fast-growing AI and ML landscape, organizations need more than just models - they need reliable, scalable, and observable systems.That’s where MLOps comes in, and where this course will help you stand out.

ThisMLOps Fundamentals Course is your practical introduction to the key practices and tools used by modern MLOps Engineers.

We go beyond theory to show you how MLOps enables real-world ML systems to run safely, efficiently, and at scale - from initial experiments to production deployment and monitoring.

Whether you’re a software engineer, DevOps practitioner, data engineer, or someone curious about operationalizing machine learning, this course is designed for you.

In this course, you will:

Learn what MLOps is and why it matters, and how it differs from traditional DevOps

Explore the entire ML model lifecycl - training, validation, deployment, monitoring, and retraining

Understand core infrastructure - Docker, Kubernetes, Cloud environments, and how they power MLOps workflows

Get introduced to essential tools like MLflow, DVC, Prometheus, Grafana, and CI/CD platforms

Discover what MLOps Engineers really do day-to-day, and how you can prepare to work in this exciting, growing field

No advanced machine learning knowledge required- this is a fundamentals course aimed at building your confidence and practical understanding.

All concepts are explained clearly and supported by examples, helping you move from curiosity to competence.

Join me and start your journey into MLOps today, gain the foundational skills to support modern AI systems, enhance your technical portfolio, and position yourself for future-ready roles in one of tech’s most in-demand fields.

Who this course is for:
- Software engineers and DevOps practitioners who want to understand the MLOps landscape
- Aspiring MLOps engineers looking for a practical introduction
- Data engineers or analysts curious about operationalizing machine learning
- Beginners or technical professionals interested in moving into ML infrastructure, automation, and production practices
More Info