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

Process Flow - Auto-scaling inference endpoints

Start: Endpoint receives requests

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Monitor request rate & resource usage

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Check if load > upper threshold?

No→Check if load < lower threshold?

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Scale up: Add more instances

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Update endpoint capacity

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Continue monitoring load

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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.