Why scaling requires different strategies in MLOps - Quick Recap

Recall & Review

beginner

What does scaling mean in the context of MLOps?

Scaling means adjusting resources or processes to handle more data, users, or workload efficiently in machine learning operations.

beginner

Why can't one scaling strategy fit all situations in MLOps?

Because different workloads, data sizes, and system architectures need specific approaches to maintain performance and cost-effectiveness.

beginner

What is horizontal scaling?

Adding more machines or instances to share the workload, like adding more cooks in a kitchen to prepare more meals.

beginner

What is vertical scaling?

Increasing the power of a single machine, like giving a cook better tools to work faster.

intermediate

How does data size affect scaling strategy in MLOps?

Large data may require distributed processing (horizontal scaling), while small data might be handled by upgrading a single machine (vertical scaling).

Which scaling strategy adds more machines to handle workload?

A Horizontal scaling

B Vertical scaling

C Diagonal scaling

D None of the above

Horizontal scaling means adding more machines or instances to share the workload.

Why do different scaling strategies matter in MLOps?

A Because all systems are the same

B Because only one strategy exists

C Because scaling is not needed

D Because workloads and data sizes vary

Different workloads and data sizes require different scaling strategies to be efficient.

What is vertical scaling?

A Adding more machines

B Increasing power of one machine

C Reducing workload

D Splitting data

Vertical scaling means upgrading a single machine's resources.

Which scenario best fits horizontal scaling?

A Small data on one powerful machine

B Single user workload

C Large data needing distributed processing

D No data processing

Large data often requires horizontal scaling with multiple machines.

What is a key reason to choose different scaling strategies?

A To save money and improve performance

B To make systems slower

C To confuse users

D To avoid using resources

Choosing the right scaling strategy helps save costs and keeps systems efficient.

Explain why scaling in MLOps requires different strategies depending on workload and data size.

Think about how adding machines or upgrading one machine affects performance.

Describe how choosing the wrong scaling strategy can affect an MLOps system.

Consider what happens if a small system tries to handle big data without proper scaling.