Kubernetes MLflow Service Pod Connection

I have deployed a build of mlflow to a pod in my kubernetes cluster. I'm able to port forward to the mlflow ui, and now I'm attempting to test it. To do this, I am running the following test on a jupyter notebook that is running on another pod in the same cluster.

import mlflow

print("Setting Tracking Server")
tracking_uri = "http://mlflow-tracking-server.default.svc.cluster.local:5000"

mlflow.set_tracking_uri(tracking_uri)

print("Logging Artifact")
mlflow.log_artifact('/home/test/mlflow-example-artifact.png')

print("DONE")

When I run this though, I get

ConnectionError: HTTPConnectionPool(host='mlflow-tracking-server.default.svc.cluster.local', port=5000): Max retries exceeded with url: /api/2.0/mlflow/runs/get? (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object>: Failed to establish a new connection: [Errno 111] Connection refused'))

The way I have deployed the mlflow pod is shown below in the yaml and docker:

Yaml:

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: mlflow-tracking-server
  namespace: default
spec:
  selector:
    matchLabels:
      app: mlflow-tracking-server
  replicas: 1
  template:
    metadata:
      labels:
        app: mlflow-tracking-server
    spec:
      containers:
      - name: mlflow-tracking-server
        image: <ECR_IMAGE>
        ports:
        - containerPort: 5000
        env:
        - name: AWS_MLFLOW_BUCKET
          value: <S3_BUCKET>
        - name: AWS_ACCESS_KEY_ID
          valueFrom:
            secretKeyRef:
              name: aws-secret
              key: AWS_ACCESS_KEY_ID
        - name: AWS_SECRET_ACCESS_KEY
          valueFrom:
            secretKeyRef:
              name: aws-secret
              key: AWS_SECRET_ACCESS_KEY

---
apiVersion: v1
kind: Service
metadata:
  name: mlflow-tracking-server
  namespace: default
  labels:
    app: mlflow-tracking-server
  annotations:
    service.beta.kubernetes.io/aws-load-balancer-type: nlb
spec:
  externalTrafficPolicy: Local
  type: LoadBalancer
  selector:
    app: mlflow-tracking-server
  ports:
    - name: http
      port: 5000
      targetPort: http

While the dockerfile calls a script that executes the mlflow server command: mlflow server --default-artifact-root ${AWS_MLFLOW_BUCKET} --host 0.0.0.0 --port 5000, I cannot connect to the service I have created using that mlflow pod.

I have tried using the tracking uri http://mlflow-tracking-server.default.svc.cluster.local:5000, I've tried using the service EXTERNAL-IP:5000, but everything I tried cannot connect and log using the service. Is there anything that I have missed in deploying my mlflow server pod to my kubernetes cluster?