Cost optimization at scale in MLOps - Mini Project: Build & Apply

Cost optimization at scale

📖 Scenario: You work as an MLOps engineer managing cloud resources for machine learning projects. Your goal is to optimize costs by tracking resource usage and identifying expensive resources to reduce waste.

🎯 Goal: Build a simple Python program that stores resource costs, sets a cost threshold, filters resources exceeding the threshold, and prints them out. This helps identify which resources to optimize or shut down.

📋 What You'll Learn

Create a dictionary with resource names and their monthly costs

Add a variable for the cost threshold

Filter resources with costs above the threshold using a dictionary comprehension

Print the filtered expensive resources

💡 Why This Matters

🌍 Real World

Cloud and MLOps engineers often need to monitor and optimize resource costs to save money and improve efficiency.

💼 Career

This project teaches basic cost tracking and filtering skills useful for managing cloud budgets and optimizing machine learning infrastructure.

1

Create resource cost dictionary

Create a dictionary called resource_costs with these exact entries: 'GPU_Cluster': 1200, 'Data_Storage': 300, 'API_Server': 450, 'Model_Training': 900, 'Monitoring': 150

MLOps

# Create the resource_costs dictionary with exact entries
# Your code here

Hint

Use curly braces to create a dictionary with keys as resource names and values as costs.

2

Set cost threshold

Create a variable called cost_threshold and set it to 500

MLOps

resource_costs = {
    'GPU_Cluster': 1200,
    'Data_Storage': 300,
    'API_Server': 450,
    'Model_Training': 900,
    'Monitoring': 150
}
# Create cost_threshold variable and set it to 500
# Your code here

Hint

Just assign the number 500 to the variable named cost_threshold.

3

Filter expensive resources

Create a dictionary called expensive_resources using a dictionary comprehension that includes only entries from resource_costs where the cost is greater than cost_threshold

MLOps

resource_costs = {
    'GPU_Cluster': 1200,
    'Data_Storage': 300,
    'API_Server': 450,
    'Model_Training': 900,
    'Monitoring': 150
}
cost_threshold = 500
# Create expensive_resources dictionary with costs above cost_threshold
# Your code here

Hint

Use a dictionary comprehension with for name, cost in resource_costs.items() and an if condition.

4

Print expensive resources

Write a print statement to display the expensive_resources dictionary

MLOps

resource_costs = {
    'GPU_Cluster': 1200,
    'Data_Storage': 300,
    'API_Server': 450,
    'Model_Training': 900,
    'Monitoring': 150
}
cost_threshold = 500
expensive_resources = {name: cost for name, cost in resource_costs.items() if cost > cost_threshold}
# Print the expensive_resources dictionary
# Your code here

Hint

Use print(expensive_resources) to show the filtered dictionary.

Practice

1. What is the main goal of cost optimization at scale in MLOps?

easy

A. To increase the number of servers regardless of workload

B. To avoid monitoring costs after deployment

C. To use only the most expensive cloud resources

D. To save money by matching resource use to workload needs

Solution

1. Step 1: Understand cost optimization purpose

   Cost optimization means using resources efficiently to reduce expenses.

2. Step 2: Match resources to workload needs

   Adjusting resources based on workload avoids waste and saves money.

3. Final Answer:

   To save money by matching resource use to workload needs -> Option D

4. Quick Check:

   Cost optimization = save money by matching resources [OK]

Hint: Cost optimization means using just enough resources [OK]

Common Mistakes:

• Thinking more servers always means better
• Ignoring cost monitoring after deployment
• Assuming expensive resources are always best

2. Which of the following is a correct way to specify a spot instance in a Kubernetes pod spec for cost savings?

easy

A. affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: "kubernetes.io/lifecycle"
operator: In
values:
- spot

B. tolerations:
- key: "spot-instance"
operator: Exists
effect: NoSchedule

C. nodeSelector:
kubernetes.io/instance-type: spot

D. resources:
requests:
cpu: "spot"
memory: "spot"

Solution

1. Step 1: Understand spot instance labeling in Kubernetes

   Spot instances are often labeled with lifecycle=spot to identify cheaper nodes.

2. Step 2: Check node affinity syntax

   affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: "kubernetes.io/lifecycle" operator: In values: - spot correctly uses nodeAffinity with matchExpressions to select nodes labeled as spot.

3. Final Answer:

   affinity with nodeSelectorTerms matching lifecycle=spot label -> Option A

4. Quick Check:

   Spot instance selection uses nodeAffinity with lifecycle=spot label [OK]

Hint: Use nodeAffinity with lifecycle=spot label for spot nodes [OK]

Common Mistakes:

• Using nodeSelector with wrong label key
• Setting resource requests to 'spot' (invalid)
• Confusing tolerations with node affinity

3. Given this autoscaling configuration snippet for a Kubernetes deployment:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: ml-model-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: ml-model
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50

What happens when CPU usage rises to 75%?

medium

A. The number of pods will increase up to a maximum of 10

B. The number of pods will decrease to 2

C. The deployment will restart pods

D. Nothing changes because CPU target is 50%

Solution

1. Step 1: Understand Horizontal Pod Autoscaler (HPA) behavior

   HPA increases pods when CPU usage exceeds target utilization to balance load.

2. Step 2: Analyze CPU usage vs target

   CPU is at 75%, above the 50% target, so HPA will scale up pods up to maxReplicas (10).

3. Final Answer:

   The number of pods will increase up to a maximum of 10 -> Option A

4. Quick Check:

   CPU > target utilization triggers pod scaling up [OK]

Hint: CPU above target utilization triggers scaling up [OK]

Common Mistakes:

• Thinking pods scale down when CPU rises
• Confusing pod restart with scaling
• Assuming no change if CPU exceeds target

4. You have a cloud cost alert system but it keeps sending false alarms about overspending. What is the most likely cause?

medium

A. The cloud provider is charging incorrectly

B. The alert thresholds are set too low or too sensitive

C. The system is not connected to the billing API

D. The cost data is updated only once a year

Solution

1. Step 1: Understand alert system sensitivity

   Alerts trigger when costs exceed set thresholds; too low thresholds cause false alarms.

2. Step 2: Evaluate other options

   Incorrect charges or missing billing data cause different issues, not false alarms.

3. Final Answer:

   The alert thresholds are set too low or too sensitive -> Option B

4. Quick Check:

   Low alert thresholds cause false alarms [OK]

Hint: Check alert thresholds if false alarms occur [OK]

Common Mistakes:

• Blaming cloud provider without proof
• Ignoring alert configuration
• Assuming billing API is always connected

5. You want to reduce costs for a large ML training job that runs daily on cloud GPUs. Which combined strategy best optimizes cost at scale?

hard

A. Run training on CPUs to avoid GPU costs without changing code

B. Use only on-demand GPU instances and disable autoscaling

C. Use spot GPU instances with checkpointing and autoscaling to handle interruptions

D. Schedule training during peak hours to use full capacity

Solution

1. Step 1: Identify cost-saving options for GPU jobs

   Spot instances are cheaper but can be interrupted; checkpointing saves progress.

2. Step 2: Combine autoscaling with spot instances and checkpointing

   Autoscaling adjusts resources; checkpointing prevents data loss on interruptions.

3. Step 3: Evaluate other options

   On-demand is costly; CPUs are slower; peak hours usually cost more.

4. Final Answer:

   Use spot GPU instances with checkpointing and autoscaling to handle interruptions -> Option C

5. Quick Check:

   Spot + checkpoint + autoscale = best cost optimization [OK]

Hint: Combine spot instances with checkpointing and autoscaling [OK]

Common Mistakes:

• Ignoring interruptions on spot instances
• Using expensive on-demand only
• Running on CPUs without code changes
• Scheduling during costly peak hours