> ## Content Index > Fetch the complete content index at: https://devopscube.com/llms.txt > Use this file to discover other available public pages before exploring further. # Setup Evidently AI on Kubernetes for ML Model Monitoring - URL: https://devopscube.com/evidently-ai-on-kubernetes/ - Published: 2026-08-01T03:40:49.000Z - Updated: 2026-08-01T03:40:49.000Z - Author: Bibin Wilson - Tags: AI/ML, HOW TO GUIDES, MLOPS, #blog Here is what we are going to do in this setup. 1. Setup Evidently platform with PostgreSQL using our custom helm chart. 2. Test model monitoring using Evidently AI library using simulated data. 3. Validate model metrics in Evidently AI dashboard. Lets get started. ## Setup Prerequisites Below are the prerequisites for this setup - [Kubernetes cluster](https://devopscube.com/setup-kubernetes-cluster-kubeadm/) - [kubectl](https://devopscube.com/kubectl-set-context/) - [Helm](https://devopscube.com/create-helm-chart/) - Python 3.11+ ## Setup Evidently AI Platform on Kubernetes Below are the steps to deploy Evidently AI on a Kubernetes cluster. ### Step 1: Clone the Repository We have pushed the Helm chart to deploy Evidently AI and a test file to show how model drift is calculated in our [MLOps Github Repository](https://github.com/techiescamp/mlops-for-devops.git?ref=devopscube.com). Fork and clone the repository using the following command. ```bash git clone https://github.com/techiescamp/mlops-for-devops.git cd mlops-for-devops ``` The files we are going to use in this blog are shown in the structure below. ```bash . ├──platform-tools │ └──evidently-ai | ├── helm │ ├── Chart.yaml │ ├── values.yaml │ └── templates | ├── psql.yaml └──phase-2-enterprise-setup └──evidently-ai └── model-monitoring.py ``` ### Step 2 : Customize the Evidently Helm Values File Before deploying the Helm chart, customize the values file. Assuming you are inside the `mlops-for-devops` directory, move to the `platform-tools/evidently-ai` directory using the following command. ```bash cd platform-tools/evidently-ai ``` There, you can find the Helm values file `values.yaml`. In the values file, - We have evidently as the PostgreSQL username and password. Replace it if you want to use different credentials. - Change the service NodePort if its already in use. The default one is **`30081`** - Change the volume size if needed - When we are writing this blog, the latest image tag is **`0.7.21`**, update the image tag if a new version is pushed. The container image we used in the Helm chart is evidently-ai's own [image](https://hub.docker.com/r/evidently/evidently-service?ref=devopscube.com). ⚠️ Ensure you have default StorageClass in the cluster. Or create one. ### Step 3: Install the Evidently AI Helm Chart Now that we have everything needed for evidently AI deployment, lets deploy the Helm Chart. Make sure you are inside the **`platform-tools/evidently-ai/helm`** directory, when running the following Helm install command. ```bash helm install evidently . ``` This will deploy the evidently-ai pod in the evidently namespace along with a PostgreSQL pod. Use the following command to list the pods. ```bash kubectl get pods -n evidently ``` You will get the following output. ```bash NAME READY STATUS RESTARTS AGE evidently-ui-7d9f5b8c6-xvn2p 1/1 Running 0 2m evidently-postgres-0 1/1 Running 0 2m ``` ### Step 4: Access the Evidently UI Now, let's access the UI of Evidently. Since we exposed the service on `NodePort 30081`, the URL for the UI will be, ```bash http://:30081 ``` To get the external IP of any node in your cluster, use the following command ```bash kubectl get nodes -o wide ``` Or you can using port forwading option to access the UI. Replace **`pods/evidently-ui-5d947b9568-xckrg`** with your pod name. ```bash kubectl port-forward pods/evidently-ui-5d947b9568-xckrg -n evidently 8000:8000 ``` If you access the UI on the browser using the [http://localhost:8000](http://localhost:8000/?ref=devopscube.com) or NodePort URL, you will see the UI as shown below. ![evidently ai dashboard](https://storage.ghost.io/c/5f/2f/5f2f4d20-2abf-4534-8d40-7aa233aedd43/content/images/2026/06/image-313.png) ⚠️ Self hosted Evidenlty AI does not have a login feature, so anyone with the publicly exposed **`NodePort`**, so in production use Ingress to access it securely. ## Model Monitoring Using Evidently AI 💡 In production, the monitoring is run using a scheduled batch job, it can be hourly, daily, or weekly, based on the traffic For testing the model monitoring pipeline, we will use a Python Script to send simulated values to detect data changes. The Python Script we are going to use is inside the `phase-2-enterprise-setup/evidently-ai` directory. Here is what the script does. - It generates 4000 synthetic rows with 8 features. In production, this data would come from real user requests hitting KServe. - It then splits the data 40/60\. The 40% trains the model. The 60% emulates the production inference dataset. - It connects to the Evidently project UUID and lays out panels for counters for row count, accuracy, ROC AUC, and drift share Assuming you are inside the `platform-tools/evidently-ai` directory, run the following command to get into `phase-2-enterprise-setup/evidently-ai` directory. ```bash cd ../../phase-2-enterprise-setup/evidently-ai/ ``` ### Step 1: Set up a Virtual Environment Once you are inside the `phase-2-enterprise-setup/evidently-ai` directory, create a virtual environment and activate it. ```Python python3 -m venv venv source venv/bin/activate ``` Then, install the required packages using the following pip command. ``` pip install evidently scikit-learn pandas ``` ### Step 2: Create a Project First, we need to create a project in our self-hosted Evidently AI. For that click on the **`+`** button, which will ask you for a **`name and description`**, enter it, and click the **`Save`** button to create a project. ![create project on evidently ai](https://storage.ghost.io/c/5f/2f/5f2f4d20-2abf-4534-8d40-7aa233aedd43/content/images/2026/06/image-314.png) Once the project is created, click the project you created, and you can see the following. ![evidently ai project id](https://storage.ghost.io/c/5f/2f/5f2f4d20-2abf-4534-8d40-7aa233aedd43/content/images/2026/06/image-316.png) ⚠️ Copy the project ID, which is needed in the next step. Also, inside the project, you can see 5 tabs: - **Dashboard** \- Here we can see how much the data has drifted as a value, a chart, or a graph. - **Reports** \- Here we can see the data pushed for each, and the information about the specific run is stored in a unique ID. - **Datasets** \- Contains raw data files that are uploaded to it. - **Traces -** It traces the LLM calls. - **Prompts** \- Used to monitor and evaluate the prompts sent to LLMs. ### Step 3: Run the Python Script Before running, open the script and update it with the `EVIDENTLY_URL` and `PROJECT_ID`, which you copied in the previous step. ![update evidently ai python script](https://storage.ghost.io/c/5f/2f/5f2f4d20-2abf-4534-8d40-7aa233aedd43/content/images/2026/06/image-319.png) Then run the following command to generate data and push to Evidently AI. ```bash python model-monitoring.py --backfill 7 ``` 💡 The script only pushes single run data when we run it. We configured a **`--backfill 7`** flag to make it push simulated data of 7 snapshots in one run, so we can see 7 different types of data to compare in the dashboard. You can change the number if needed. If the script runs successfully, you will get an output similar to this. ```bash Connecting to http://54.202.77.42:30081/ Using project: 019efd7f-f8ef-7583-bca6-8ba39ea60e68 Dashboard built. Training model and reference data ... Uploaded reference dataset. Backfilling 7 day(s) of history ... pushed snapshot @ 2026-06-19 pushed snapshot @ 2026-06-20 pushed snapshot @ 2026-06-21 pushed snapshot @ 2026-06-22 pushed snapshot @ 2026-06-23 pushed snapshot @ 2026-06-24 pushed snapshot @ 2026-06-25 Pushing current monitoring tick ... pushed snapshot @ 2026-06-25 12:04:57 Done. Open the project -> Dashboard tab (refresh if needed). Run again — or schedule it — to keep adding points. ``` ### Step 4: Verify the Model Monitoring Metrics in Evidently AI Dashboard Once the run is successful, open the Evidently UI, and you can see the dashboards updated in the project which you created. ![](https://storage.ghost.io/c/5f/2f/5f2f4d20-2abf-4534-8d40-7aa233aedd43/content/images/2026/07/image-123.png) If you scroll down you can see the prediction metrics (F1 score, Recall, Precision and Accuracy) in graph format as shown below. ![F1 score, Recall, Precision and Accuracy graph in evidently AI dashbaord](https://storage.ghost.io/c/5f/2f/5f2f4d20-2abf-4534-8d40-7aa233aedd43/content/images/2026/08/image.png) You can also check reports of each data and datasets under each tabs. ![tracking model perforamce using evidently ai](https://storage.ghost.io/c/5f/2f/5f2f4d20-2abf-4534-8d40-7aa233aedd43/content/images/2026/06/image-318.png) ## Conclusion In this blog, we have learned how machine learning model monitoring works using Evidently AI. Also, we have self-hosted it on [Kubernetes](https://devopscube.com/kubernetes-tutorials-beginners/) and sent sample data to check how it detects data drift. In simple terms, your application monitoring tools tell you if they are up and working properly, and model monitoring tools tell you that the model is still giving the output accurately.