Cost allocation and optimization in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is cost allocation in MLOps?

Cost allocation in MLOps means dividing and assigning the expenses of running machine learning workflows to different teams, projects, or departments to understand who uses what resources.

beginner

Name one common method to optimize costs in MLOps.

One common method is to use spot instances or preemptible VMs, which are cheaper cloud compute resources that can be interrupted but save money when used for non-critical tasks.

intermediate

Why is tagging resources important for cost allocation?

Tagging resources with labels like project name or team helps track and report costs accurately, making it easier to allocate expenses to the right groups.

intermediate

How can monitoring help in cost optimization?

Monitoring usage and spending helps spot waste like idle machines or over-provisioned resources, so you can adjust and reduce unnecessary costs.

intermediate

What role does automation play in cost optimization for MLOps?

Automation can schedule resources to run only when needed and shut down idle services, which saves money by avoiding paying for unused compute or storage.

What is the main goal of cost allocation in MLOps?

A Assign expenses to the right teams or projects

B Increase cloud spending

C Remove all cloud resources

D Ignore resource usage

Cost allocation helps assign expenses to the correct teams or projects to understand and manage spending.

Which of these is a cost optimization strategy?

A Keeping all machines running 24/7

B Using spot instances for non-critical tasks

C Ignoring resource tags

D Avoiding monitoring tools

Spot instances are cheaper and good for tasks that can handle interruptions, saving money.

Why should you tag cloud resources in MLOps?

A To increase resource usage

B To make resources invisible

C To track and allocate costs properly

D To delete resources automatically

Tags help identify which team or project uses which resource, enabling accurate cost allocation.

What does monitoring resource usage help with?

A Adding more resources unnecessarily

B Increasing idle time

C Ignoring spending patterns

D Finding waste and reducing costs

Monitoring helps find unused or overused resources so you can optimize spending.

How does automation reduce costs in MLOps?

A By shutting down idle resources automatically

B By running all resources nonstop

C By ignoring usage data

D By increasing manual work

Automation can turn off resources when not needed, saving money.

Explain how cost allocation helps teams manage cloud expenses in MLOps.

Think about how you would split a restaurant bill fairly among friends.

Describe three ways to optimize costs when running machine learning workflows in the cloud.

Imagine ways to save money on your phone bill by using cheaper plans and turning off unused apps.