Cost optimization strategies in Agentic AI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is cost optimization in machine learning projects?

Cost optimization means finding ways to reduce the money spent on computing resources, data storage, and other expenses while keeping the model's performance good.

beginner

Why is using smaller datasets sometimes a good cost optimization strategy?

Smaller datasets reduce the time and computing power needed to train models, which lowers costs. But it should still keep enough data to learn well.

intermediate

How does model pruning help in cost optimization?

Model pruning removes unnecessary parts of a model to make it smaller and faster. This reduces the computing resources needed, saving money.

intermediate

What role do cloud spot instances play in cost optimization?

Spot instances are cheaper cloud computers that can be interrupted. Using them for training can save money if the job can handle interruptions.

intermediate

Explain how automated hyperparameter tuning can reduce costs.

Automated tuning finds the best model settings faster than manual trial and error, saving time and computing power, which lowers costs.

Which of the following is a direct way to reduce training costs?

A Increasing model complexity

B Using smaller batch sizes

C Training on larger datasets

D Using more epochs without early stopping

Smaller batch sizes reduce memory use and can speed up training, lowering costs.

What is a risk when using cloud spot instances for training?

A Job interruptions causing restarts

B Slower training speed

C Higher cost than regular instances

D No access to GPUs

Spot instances can be stopped anytime, so jobs must handle interruptions.

Which technique reduces model size to save cost?

A Data augmentation

B Adding dropout

C Model pruning

D Increasing layers

Model pruning removes unnecessary parts to make the model smaller and cheaper to run.

Why is early stopping useful for cost optimization?

A It uses more data for training

B It increases training time

C It adds more layers to the model

D It stops training when performance stops improving

Early stopping saves resources by ending training once the model stops improving.

Automated hyperparameter tuning helps cost optimization by:

A Efficiently finding best settings faster

B Manually testing each setting

C Randomly guessing parameters

D Ignoring model performance

Automated tuning quickly finds good settings, reducing wasted time and cost.

Describe three strategies to optimize costs in machine learning projects.

Think about data size, model size, cloud options, and training control.

Explain how early stopping and model pruning contribute to cost savings.

Focus on training duration and model size.