Cost optimization at scale in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is cost optimization in MLOps?

Cost optimization in MLOps means using resources like computing power and storage efficiently to reduce expenses while keeping model performance and reliability high.

beginner

Why is monitoring resource usage important for cost optimization?

Monitoring helps spot when resources are overused or wasted, so you can adjust and avoid paying for more than you need.

intermediate

How can autoscaling help reduce costs in MLOps?

Autoscaling adjusts the number of machines running your models based on demand, so you only pay for what you use, avoiding idle resources.

intermediate

What role do spot instances or preemptible VMs play in cost optimization?

They offer cheaper computing power but can be interrupted, so they are good for flexible or non-critical tasks to save money.

advanced

Explain the benefit of model optimization techniques for cost reduction.

Techniques like pruning or quantization make models smaller and faster, which lowers the computing resources needed and cuts costs.

What is a simple way to avoid paying for unused computing resources in MLOps?

A Use autoscaling to match resources to demand

B Always run maximum number of machines

C Ignore resource usage reports

D Use only on-demand instances

Autoscaling adjusts resources based on demand, preventing waste and saving costs.

Which type of instance is cheaper but can be stopped unexpectedly?

A Spot instance or preemptible VM

B Reserved instance

C On-demand instance

D Dedicated host

Spot instances or preemptible VMs are cheaper but can be interrupted, suitable for flexible workloads.

Why is monitoring important for cost optimization?

A To disable autoscaling

B To increase resource usage

C To ignore cost reports

D To identify waste and optimize spending

Monitoring helps find where resources are wasted so you can reduce unnecessary costs.

What does model quantization do to help reduce costs?

A Makes models bigger and slower

B Increases resource usage

C Makes models smaller and faster

D Removes model accuracy

Quantization reduces model size and speeds up inference, lowering resource needs and costs.

Which practice helps ensure you only pay for what you use in cloud computing?

A Static resource allocation

B Autoscaling

C Ignoring usage data

D Running all jobs at once

Autoscaling dynamically adjusts resources to match workload, optimizing cost.

Describe three strategies to optimize costs when running machine learning models at scale.

Think about adjusting resources, cheaper compute options, and making models efficient.

Explain why monitoring resource usage is critical for cost optimization in MLOps environments.

Consider how knowing what you use helps control costs.