Self-service ML platform architecture in MLOps - Commands & Configuration

Introduction

Building machine learning models often requires many steps and tools. A self-service ML platform architecture helps teams easily create, train, and deploy models without needing deep technical help every time.

When data scientists want to train models without waiting for IT support

When multiple teams need to share ML tools and resources efficiently

When you want to automate model deployment to production quickly

When you want to track experiments and results in one place

When you want to reuse code and data pipelines across projects

Config File - mlflow_tracking_server.yaml

mlflow_tracking_server.yaml

apiVersion: v1
kind: Service
metadata:
  name: mlflow-tracking
  labels:
    app: mlflow
spec:
  type: ClusterIP
  ports:
    - port: 5000
      targetPort: 5000
      protocol: TCP
      name: http
  selector:
    app: mlflow
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: mlflow-tracking
spec:
  replicas: 1
  selector:
    matchLabels:
      app: mlflow
  template:
    metadata:
      labels:
        app: mlflow
    spec:
      containers:
      - name: mlflow
        image: mlflow/mlflow:2.5.0
        ports:
        - containerPort: 5000
        command: ["mlflow", "server", "--backend-store-uri", "sqlite:///mlflow.db", "--default-artifact-root", "/mlflow/artifacts", "--host", "0.0.0.0"]
        volumeMounts:
        - name: mlflow-artifacts
          mountPath: /mlflow/artifacts
      volumes:
      - name: mlflow-artifacts
        emptyDir: {}

This Kubernetes YAML file deploys an MLflow tracking server as a self-service ML platform component.

• Service: Exposes MLflow server inside the cluster on port 5000.
• Deployment: Runs one replica of the MLflow server container.
• Command: Starts MLflow server with SQLite backend and artifact storage in a temporary directory.

Commands

This command creates the MLflow tracking server deployment and service in the Kubernetes cluster so the platform is available for users.

Terminal

kubectl apply -f mlflow_tracking_server.yaml

Expected Output

deployment.apps/mlflow-tracking created

service/mlflow-tracking created

Check that the MLflow tracking server pod is running and ready for use.

Terminal

kubectl get pods -l app=mlflow

Expected Output

NAME                              READY   STATUS    RESTARTS   AGE

mlflow-tracking-7d8f9c7f5b-abcde 1/1     Running   0          30s

→

-l - Filter pods by label

Create a new MLflow experiment to organize and track machine learning runs.

Terminal

mlflow experiments create --experiment-name my-first-experiment

Expected Output

Created experiment with ID 1

→

--experiment-name - Name the new experiment

Run a sample MLflow project from a GitHub repo with a parameter to train a model and log results automatically.

Terminal

mlflow run https://github.com/mlflow/mlflow-example.git -P alpha=0.5

Expected Output

2024/06/01 12:00:00 INFO mlflow.projects: === Run (ID '1234567890abcdef') succeeded ===

→

-P - Set parameters for the run

Key Concept

If you remember nothing else from this pattern, remember: a self-service ML platform lets teams run, track, and share ML work easily without waiting for infrastructure help.

Code Example

MLOps

import mlflow

mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("my-first-experiment")

with mlflow.start_run():
    mlflow.log_param("alpha", 0.5)
    mlflow.log_metric("rmse", 1.23)
    print("Run logged successfully")

Common Mistakes

Not exposing the MLflow server service correctly in Kubernetes

Users cannot access the tracking UI or API, so the platform is unusable

Create a Kubernetes Service with the correct port and selector to expose MLflow

Running MLflow without specifying backend store and artifact root

MLflow cannot save experiment data or artifacts, losing all tracking info

Always specify --backend-store-uri and --default-artifact-root when starting MLflow server

Not creating experiments before running MLflow projects

Runs get logged to default experiment, making organization and comparison difficult

Create named experiments to keep runs organized

Summary

Deploy an MLflow tracking server to provide a self-service ML platform.

Verify the server is running and accessible inside the cluster.

Create experiments to organize ML runs.

Run ML projects that log parameters and metrics automatically.