diff --git a/figures/a_b_testing.svg b/figures/a_b_testing.svg
new file mode 100644
index 000000000..bb407beb8
--- /dev/null
+++ b/figures/a_b_testing.svg
@@ -0,0 +1,710 @@
+
+
diff --git a/figures/a_b_testing_example.png b/figures/a_b_testing_example.png
new file mode 100644
index 000000000..4cfe49ef0
Binary files /dev/null and b/figures/a_b_testing_example.png differ
diff --git a/figures/canary_deployment.svg b/figures/canary_deployment.svg
new file mode 100644
index 000000000..a6e8bbee9
--- /dev/null
+++ b/figures/canary_deployment.svg
@@ -0,0 +1,2092 @@
+
+
diff --git a/figures/shadow_testing.svg b/figures/shadow_testing.svg
new file mode 100644
index 000000000..565c64d1f
--- /dev/null
+++ b/figures/shadow_testing.svg
@@ -0,0 +1,570 @@
+
+
diff --git a/mkdocs.yml b/mkdocs.yml
index 1c52e76c9..b6aa32820 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -64,6 +64,7 @@ plugins:
- search
- glightbox
- same-dir
+ - inline-svg
- git-revision-date-localized:
enable_creation_date: true
- exclude:
@@ -145,6 +146,7 @@ nav:
- M33 - High Performance Clusters: s10_extra/high_performance_clusters.md
- M34 - Frontend: s10_extra/frontend.md
- M35 - ML deployment: s10_extra/ml_deployment.md
+ - M36 - Deployment Testing: s7_deployment/deployment_testing.md
# - M35 - Designing Pipelines: s10_extra/design.md
# - M37 - Workflow orchestration: s10_extra/orchestration.md
# - M38 - Kubernetes: s10_extra/kubernetes.md
diff --git a/requirements.txt b/requirements.txt
index 9e577470f..5e0f0c84b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,6 +6,7 @@ pymdown-extensions>=1.1.1
mkdocs-same-dir>=0.1.2
mkdocs-git-revision-date-localized-plugin>=1.2.6
mkdocs-exclude>=1.0.2
+mkdocs-plugin-inline-svg>=0.1.0
# Developer stuff
ruff>=0.4.7
diff --git a/s7_deployment/deployment_testing.md b/s7_deployment/deployment_testing.md
new file mode 100644
index 000000000..99dd6ebe5
--- /dev/null
+++ b/s7_deployment/deployment_testing.md
@@ -0,0 +1,189 @@
+{ align=right width="130"}
+
+# Deployment Testing
+
+In the module on [testing of APIs](testing_apis.md) we learned how to write integration tests for our APIs and how to
+run loadtests to see how our API behaves under different loads. In this module we are going to learn about a few
+different deployment testing strategies that can be used to ensure that our application is working as expected *before*
+we deploy it to production. Deployment testing is designed to minimize risk, enhance performance, and ensure a smooth
+transition from development to production. Being able to choose and execute the correct deployment testing strategy is
+even more important within machine learning projects as we tend to update our models more frequently than traditional
+software projects, thus requiring more frequent deployments.
+
+In general we recommend you start out with reading this
+[page](https://cloud.google.com/architecture/application-deployment-and-testing-strategies) from GCP on both application
+deployment and testing strategies. It is a good read on the different metrics we can use to evaluate our deployment
+(down time, rollback duration, etc.) and the different deployment strategies we can use. In the following we are going
+to be looking at the three testing methods
+
+* A/B testing
+* Canary deployment
+* Shadow deployment
+
+## A/B testing
+
+In software development, [A/B testing](https://en.wikipedia.org/wiki/A/B_testing) is a method of comparing two versions
+of a web page or application against each other to determine which one performs better. A/B testing is a form of
+statistical hypothesis testing with two variants
+
+
+
+
+In this case we are randomly A/B testing if the color and style of the `Learn more` button affects the click rate. In
+this hypothetical example, the green button has a 20% higher click rate than the blue button and is therefore the
+preferred choice for the final design.
+ Image credit
+
+
+
+
+
+
+
+ Image credit
+
+
+
+[text](https://www.surveymonkey.com/mp/ab-testing-significance-calculator/)
+
+
+Assumed Distribution | Example case | Standard test
+-------------------- | ------------ | -------------
+Gaussian | Average revenue per user | t-test
+Binomial | Click-through rate | Fisher's exact test
+Poisson | Number of purchases | Chi-squared test
+Multinomial | User preferences | Chi-squared test
+Unknown | Time to purchase | Mann-Whitney U test
+
+
+### ❔ Exercises
+
+In the exercises we are going to perform two different kinds of A/B testing. The first one is going to be a simple
+A/B test where we are going to test two different versions of the same service. The second
+
+1. There are a multiple ways to implement A/B testing.
+
+ ```python
+ from fastapi import FastAPI, Request, HTTPException
+ import geoip2.database
+
+ app = FastAPI()
+
+ # Load the GeoLite2 database
+ reader = geoip2.database.Reader('/path/to/GeoLite2-City.mmdb')
+
+ def get_client_ip(request: Request) -> str:
+ if "X-Forwarded-For" in request.headers:
+ return request.headers["X-Forwarded-For"].split(",")[0]
+ if "X-Real-IP" in request.headers:
+ return request.headers["X-Real-IP"]
+ return request.client.host
+
+ def get_geolocation(ip: str) -> dict:
+ try:
+ response = reader.city(ip)
+ return {
+ "ip": ip,
+ "city": response.city.name,
+ "region": response.subdivisions.most_specific.name,
+ "country": response.country.name,
+ "location": {
+ "latitude": response.location.latitude,
+ "longitude": response.location.longitude
+ }
+ }
+ except geoip2.errors.AddressNotFoundError:
+ raise HTTPException(status_code=404, detail="IP address not found in the database")
+ except Exception as e:
+ raise HTTPException(status_code=500, detail=str(e))
+
+ @app.get("/geolocation")
+ async def geolocation(request: Request):
+ client_ip = get_client_ip(request)
+ geolocation_data = get_geolocation(client_ip)
+ return geolocation_data
+ ```
+
+## Canary deployment
+
+
+
+
+ Image credit
+
+
+
+
+### ❔ Exercises
+
+Follow [these](https://cloud.google.com/architecture/implementing-cloud-run-canary-deployments-git-branches-cloud-build)
+instructions to implement a canary deployment using Git branches and Cloud Build.
+
+1. Use the following command
+
+ ```bash
+ gcloud run services update-traffic
+ ```
+
+## Shadow deployment
+
+
+
+
+ Image credit
+
+
+
+
+### ❔ Exercises
+
+1. Google Run does not naturally support shadow deployments, because its loadbalancer requires that the traffic adds up
+ to 100%, and for shadow deployments, it would be 200%. To proper implement this you would need to use a kubernetes
+ cluster and use a service mesh like Istio. So instead we are going to implement a very simple load balancer ourself.
+
+ 1. Create a new script called `loadbalancer.py` and add the following code
+
+ ```python
+ import random
+ from fastapi import FastAPI, HTTPException
+ import requests
+
+ app = FastAPI()
+
+ services = {
+ "service1": "http://localhost:8000",
+ "service2": "http://localhost:8001"
+ }
+
+ @app.get("/shadow")
+ async def shadow():
+ service = random.choice(list(services.keys()))
+ response = requests.get(services[service] + "/shadow")
+ return {
+ "service": service,
+ "response": response.json()
+ }
+ ```
+
+ 2. Because the loadbalancer is just a simple Python script lets just deploy it to Cloud Functions instead of Cloud
+ Run. Create a new Cloud Function and deploy the script.
+
+## 🧠 Knowledge check
+
+1. Try to fill out the following table:
+
+ Testing patter | Zero downtime | Real production traffic testing | Releasing to users based on conditions | Rollback duration | Releasing to users based on conditions |
+ --------------- | -------------- | -------------------------------- | -------------------------------------- | ----------------- | -------------------------------------- |
+ A/B testing | | | | | |
+ Canary deployment | | | | | |
+ Shadow deployment | | | | | |
+
+ ??? success "Solution"
+
+ Testing patter | Zero downtime | Real production traffic testing | Releasing to users based on conditions | Rollback duration | Releasing to users based on conditions |
+ --------------- | -------------- | -------------------------------- | -------------------------------------- | ----------------- | -------------------------------------- |
+ A/B testing | No | No | Yes | Short | No |
+ Canary deployment | Yes | Yes | Yes | Short | Yes |
+ Shadow deployment | Yes | No | Yes | Short | Yes |
+
+This ends the deployment testing module.