Auto-scaling inference endpoints in MLOps - Step-by-Step Execution

Process Flow - Auto-scaling inference endpoints

Start: Endpoint receives requests

↓

Monitor request rate & resource usage

↓

Check if load > upper threshold?

No→Check if load < lower threshold?

↓

Scale up: Add more instances

↓

Update endpoint capacity

↓

Continue monitoring load

↓

End

The system monitors traffic and resource use, then scales the number of inference instances up or down to match demand automatically.

Execution Sample

MLOps

requests = [10, 50, 120, 80, 30, 5]
instances = 1
for load in requests:
    if load > 100:
        instances += 1
    elif load < 20 and instances > 1:
        instances -= 1
    print(f"Load: {load}, Instances: {instances}")

Simulates auto-scaling instances based on incoming request load.

Process Table

Step	Load (requests)	Condition: load > 100	Condition: load < 20 and instances > 1	Action	Instances after action	Output
1	10	False	False (instances=1)	No scaling	1	Load: 10, Instances: 1
2	50	False	False	No scaling	1	Load: 50, Instances: 1
3	120	True	False	Scale up by 1	2	Load: 120, Instances: 2
4	80	False	False	No scaling	2	Load: 80, Instances: 2
5	30	False	False	No scaling	2	Load: 30, Instances: 2
6	5	False	True (instances=2)	Scale down by 1	1	Load: 5, Instances: 1

💡 All loads processed; scaling adjusted instances accordingly.

Status Tracker

Variable	Start	After 1	After 2	After 3	After 4	After 5	After 6	Final
instances	1	1	1	2	2	2	1	1
load	-	10	50	120	80	30	5	-

Key Moments - 3 Insights

Why doesn't the number of instances decrease when load is 10 at step 1?

Because instances start at 1 and the condition to scale down requires instances > 1. At step 1, instances = 1, so no scale down happens (see execution_table row 1).

Why do instances increase at step 3 when load is 120?

Load 120 is greater than the upper threshold 100, so the system scales up by adding one instance (see execution_table row 3).

Why does the system scale down at step 6?

Load 5 is less than 20 and instances are currently 2, so the system removes one instance (see execution_table row 6).

Visual Quiz - 3 Questions

Look at the execution table, what is the number of instances after processing load 80?

A 1

B 2

C 3

D 0

💡 Hint

Check the 'Instances after action' column at step 4 in the execution_table.

At which step does the condition 'load > 100' become true for the first time?

A Step 3

B Step 2

C Step 5

D Step 6

💡 Hint

Look at the 'Condition: load > 100' column in the execution_table.

If the lower threshold changed from 20 to 10, at which step would scaling down happen?

A Step 1

B No scaling down would occur

C Step 6

D Step 5

💡 Hint

Compare loads with the new threshold and check the 'Condition: load < 20 and instances > 1' logic in variable_tracker.

Concept Snapshot

Auto-scaling inference endpoints:
- Monitor request load and resource use continuously.
- If load > upper threshold, add instances.
- If load < lower threshold and instances > 1, remove instances.
- Adjust capacity dynamically to save cost and maintain performance.
- Simple thresholds guide scaling decisions.

Full Transcript

Auto-scaling inference endpoints work by watching how many requests come in and how busy the system is. When the load gets too high, it adds more instances to handle the traffic. When the load is low, it removes instances to save resources. This example code simulates this by checking each load value and changing the number of instances accordingly. The execution table shows each step's load, conditions checked, actions taken, and the resulting number of instances. Key moments explain why scaling happens or not at certain steps. The quiz tests understanding by asking about instance counts and conditions at specific steps. This helps beginners see how auto-scaling adjusts capacity automatically.

Practice

1. What is the main purpose of auto-scaling inference endpoints in ML services?

easy

A. To automatically adjust the number of servers based on traffic

B. To manually add servers when traffic increases

C. To reduce the accuracy of ML models during high traffic

D. To store more data for training models

Solution

1. Step 1: Understand auto-scaling concept

   Auto-scaling means the system changes the number of servers automatically depending on the traffic load.

2. Step 2: Identify the purpose in ML inference

   For ML inference endpoints, auto-scaling keeps the service fast and cost-efficient by adjusting servers without manual work.

3. Final Answer:

   To automatically adjust the number of servers based on traffic -> Option A

4. Quick Check:

   Auto-scaling = automatic server adjustment [OK]

Hint: Auto-scaling means automatic server count change [OK]

Common Mistakes:

• Thinking auto-scaling requires manual server changes
• Confusing auto-scaling with model accuracy changes
• Believing auto-scaling stores training data

2. Which configuration setting defines the minimum number of servers to keep running in an auto-scaling inference endpoint?

easy

A. max_servers

B. scale_up_threshold

C. target_utilization

D. min_servers

Solution

1. Step 1: Identify minimum server setting

   The minimum number of servers to keep running is controlled by the setting named min_servers.

2. Step 2: Differentiate from other settings

   max_servers sets the upper limit, target_utilization controls load target, and scale_up_threshold is not a standard setting here.

3. Final Answer:

   min_servers -> Option D

4. Quick Check:

   Minimum servers = min_servers [OK]

Hint: Min servers setting always starts with 'min_' [OK]

Common Mistakes:

• Confusing max_servers with minimum servers
• Mixing target utilization with server count
• Using non-existent settings like scale_up_threshold

3. Given this auto-scaling config snippet:

{
  "min_servers": 2,
  "max_servers": 5,
  "target_utilization": 0.7
}

If the current server usage is 80%, what will likely happen?

medium

A. The system will scale up servers to reduce load

B. The system will scale down servers to save cost

C. The system will keep the same number of servers

D. The system will shut down all servers

Solution

1. Step 1: Compare current usage to target utilization

   The current usage (80%) is higher than the target utilization (70%).

2. Step 2: Determine scaling action

   Since usage is above target, the system will add servers (scale up) to reduce load and meet the target.

3. Final Answer:

   The system will scale up servers to reduce load -> Option A

4. Quick Check:

   Usage > target = scale up [OK]

Hint: If usage > target, scale up servers [OK]

Common Mistakes:

• Scaling down when usage is above target
• Assuming no change if usage is slightly above target
• Thinking system shuts down servers automatically

4. You configured an auto-scaling endpoint with min_servers: 1 and max_servers: 3. The system never scales above 1 server even under high load. What is the most likely cause?

medium

A. The max_servers is set too low to allow scaling

B. The target utilization is set too high, preventing scale up

C. The min_servers value is incorrectly set to 3

D. The system does not support auto-scaling

Solution

1. Step 1: Analyze scaling limits

   Min servers is 1 and max servers is 3, so scaling up to 3 is allowed.

2. Step 2: Check target utilization impact

   If target utilization is set very high (e.g., 90%+), the system thinks current load is acceptable and won't scale up.

3. Final Answer:

   The target utilization is set too high, preventing scale up -> Option B

4. Quick Check:

   High target utilization blocks scaling up [OK]

Hint: High target utilization can block scaling up [OK]

Common Mistakes:

• Confusing max_servers as too low when it allows scaling
• Misreading min_servers as max_servers
• Assuming system lacks auto-scaling support

5. You want to configure an auto-scaling inference endpoint that never drops below 2 servers, never exceeds 6 servers, and aims to keep CPU usage around 60%. Which configuration is correct?

hard

A. { "min_servers": 2, "max_servers": 6, "target_utilization": 0.9 }

B. { "min_servers": 6, "max_servers": 2, "target_utilization": 0.6 }

C. { "min_servers": 2, "max_servers": 6, "target_utilization": 0.6 }

D. { "min_servers": 1, "max_servers": 6, "target_utilization": 0.6 }

Solution

1. Step 1: Set minimum and maximum servers correctly

   Minimum servers should be 2 and maximum servers 6, so min_servers: 2 and max_servers: 6 are correct.

2. Step 2: Set target utilization to 60%

   Target utilization should be 0.6 (60%) to keep CPU usage around that level.

3. Step 3: Verify options

   { "min_servers": 2, "max_servers": 6, "target_utilization": 0.6 } matches all requirements. { "min_servers": 6, "max_servers": 2, "target_utilization": 0.6 } reverses min and max servers. { "min_servers": 2, "max_servers": 6, "target_utilization": 0.9 } has wrong target utilization. { "min_servers": 1, "max_servers": 6, "target_utilization": 0.6 } has min_servers as 1, which is below requirement.

4. Final Answer:

   { "min_servers": 2, "max_servers": 6, "target_utilization": 0.6 } -> Option C

5. Quick Check:

   Correct min, max, and target utilization = { "min_servers": 2, "max_servers": 6, "target_utilization": 0.6 } [OK]

Hint: Min ≤ max and target_utilization as decimal (0.6) [OK]

Common Mistakes:

• Swapping min_servers and max_servers values
• Using target_utilization as percentage (60) instead of decimal (0.6)
• Setting min_servers lower than required