Canary releases for model updates in MLOps - Step-by-Step Execution

Process Flow - Canary releases for model updates

Start with current stable model

↓

Deploy new model to small % of users

↓

Monitor performance and errors

↓

Good

↓

Increase %

↓

Full rollout

This flow shows how a new model is released to a small group first, monitored, then either fully rolled out or rolled back based on results.

Execution Sample

MLOps

deploy_model(version='v2', traffic=10)
monitor_metrics()
if metrics_good:
  increase_traffic(50)
else:
  rollback_to('v1')

This code deploys a new model to 10% of users, monitors it, then increases traffic or rolls back based on metrics.

Process Table

Step	Action	Traffic % to new model	Metrics Status	Decision	Result
1	Deploy new model v2	10%	Pending	Wait	New model serving 10% users
2	Monitor metrics	10%	Good	Increase traffic	Prepare to increase rollout
3	Increase traffic to 50%	50%	Pending	Wait	New model serving 50% users
4	Monitor metrics	50%	Good	Full rollout	Prepare full rollout
5	Increase traffic to 100%	100%	Pending	Wait	New model serving all users
6	Monitor metrics	100%	Good	Complete	New model fully deployed
7	End process	100%	Good	Stop	Deployment successful

💡 Deployment ends after full rollout with good metrics or rollback if metrics were bad

Status Tracker

Variable	Start	After Step 1	After Step 3	After Step 5	Final
traffic_percent	0%	10%	50%	100%	100%
metrics_status	N/A	Pending	Pending	Pending	Good
deployment_state	stable v1	canary v2	canary v2	full v2	full v2

Key Moments - 3 Insights

Why do we start with only a small percentage of traffic to the new model?

Starting small limits risk. If the new model has issues, only a few users are affected. See execution_table step 1 where traffic is 10%.

What happens if the metrics are not good during monitoring?

If metrics are bad, the deployment is rolled back to the stable model to avoid impacting users. This is implied in the flow after monitoring steps.

Why do we increase traffic gradually instead of all at once?

Gradual increase helps catch problems early and ensures stability before full rollout. Execution_table steps 3 and 5 show traffic increasing stepwise.

Visual Quiz - 3 Questions

Look at the execution table, what is the traffic percentage to the new model at step 3?

A 50%

B 100%

C 10%

D 0%

💡 Hint

Check the 'Traffic % to new model' column at step 3 in the execution_table.

At which step does the new model start serving all users?

A Step 1

B Step 3

C Step 5

D Step 7

💡 Hint

Look for 100% traffic in the 'Traffic % to new model' column in the execution_table.

If metrics were bad at step 2, what would be the expected action?

A Increase traffic to 50%

B Rollback to stable model

C Continue monitoring without changes

D Deploy another new model

💡 Hint

Refer to the key_moments section about what happens if metrics are bad during monitoring.

Concept Snapshot

Canary releases deploy a new model to a small user group first.
Monitor performance carefully.
If good, increase traffic gradually.
If bad, rollback immediately.
This reduces risk and ensures smooth updates.

Full Transcript

Canary releases for model updates start by deploying the new model to a small percentage of users. This limits risk if the new model has issues. We then monitor key metrics like accuracy and errors. If metrics are good, we increase the traffic percentage step by step, watching performance at each stage. If metrics become bad at any point, we rollback to the stable model to protect users. This process continues until the new model serves all users or is rolled back. The execution table shows each step with traffic percentages and decisions. Variables like traffic_percent and deployment_state change as the rollout progresses. Key moments include why we start small, what happens on bad metrics, and why gradual rollout is important. The visual quiz tests understanding of these steps and decisions. This method helps update models safely and reliably.