This course is part of MLOps | Machine Learning Operations Specialization
We are actively working on FinOps for MLOps Course and it will be AVAILABLE SOON.
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What you’ll learn
✓ Understand key cost drivers in MLOps pipelines, including data, training, serving, and orchestration in cloud platforms.
✓ Apply FinOps principles to track, control, and reduce cloud costs in machine learning environments.
✓ Use tools like Kubecost, AWS Billing, and GCP cost dashboards to monitor and optimize ML infrastructure.
✓ Build a cost-aware MLOps workflow with budgeting, cost tagging, and alerts integrated into model lifecycle stages.
There are 12 modules in this course
- Introduction
- Understanding Cost Drivers
- FinOps Lifecycle
- Cost Monitoring & Budgeting
- Cloud Optimization Strategies
- Experimentation Efficiency and Cost Control
- Serving and Inference Cost Management
- FinOps Culture
- Case Studies
- Tools, Dashboards & Automation
- FinOps KPIs and Metrics
- Capstone Project
About this course
In this course, you will learn what FinOps means in the world of Machine Learning Operations (MLOps), and why cost management is a key part of running ML systems at scale. As more companies adopt ML in production, they often face high and unpredictable cloud costs. This course will help you understand how to work with money just like you work with models, data, and infrastructure.
We will begin with simple ideas – where ML costs come from, who is responsible for them, and how to make costs easier to track and control. You will explore real-life cost problems in training, deployment, and experimentation. You will also learn how MLOps engineers can build systems that are not only reliable and fast, but also efficient and cost-aware.
By the end of this course, you will know how to think about cost in every part of the ML lifecycle – from testing new ideas to serving models in production. You will understand how to bring FinOps principles into your daily work, how to talk about cost with your team, and how to avoid waste in ML pipelines.
The goal of FinOps for MLOps is clear: to give you the knowledge and habits to make smart cost decisions when building and running ML systems. Whether you are just starting with MLOps or already working in production, this course will help you understand the money side of Machine Learning Operations in a simple and practical way.
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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