diff --git a/docs/content/using-tesseracts/advanced.md b/docs/content/using-tesseracts/advanced.md
index 2708a1c5..5340b2e9 100644
--- a/docs/content/using-tesseracts/advanced.md
+++ b/docs/content/using-tesseracts/advanced.md
@@ -66,7 +66,7 @@ As an alternative to the MLflow setup we provide, you can point your Tesseract t
 $ tesseract serve --env=TESSERACT_MLFLOW_TRACKING_URI="..."  metrics
 ````
 
-Note that if your MLFlow server uses basic auth, you need to populate the `TESSERACT_MLFLOW_TRACKING_USERNAME` and
+Note that if your MLflow server uses basic auth, you need to populate the `TESSERACT_MLFLOW_TRACKING_USERNAME` and
 `TESSERACT_MLFLOW_TRACKING_PASSWORD` for the Tesseract to be able to authenticate to it.
 
 ```bash
diff --git a/examples/metrics/tesseract_requirements.txt b/examples/metrics/tesseract_requirements.txt
index 117814ce..1f797d40 100644
--- a/examples/metrics/tesseract_requirements.txt
+++ b/examples/metrics/tesseract_requirements.txt
@@ -1,4 +1,2 @@
 # Tesseract requirements file
 # Generated by tesseract 0.9.2.dev16+g7ca45a2.d20250627 on 2025-06-27T11:44:45.333107
-
-mlflow==3.1.1
diff --git a/extra/mlflow/docker-compose-mlflow.yml b/extra/mlflow/docker-compose-mlflow.yml
index 34d6e73b..606ca78e 100644
--- a/extra/mlflow/docker-compose-mlflow.yml
+++ b/extra/mlflow/docker-compose-mlflow.yml
@@ -17,15 +17,30 @@ services:
     image: ghcr.io/mlflow/mlflow:latest
     restart: unless-stopped
     user: 1000:1000
-    command: mlflow server --backend-store-uri sqlite:///mlflow-data/mlflow.db --default-artifact-root file:///mlflow-data/mlruns --host 0.0.0.0 --port 5000
+    command: >
+      mlflow server
+      --backend-store-uri sqlite:///mlflow-data/mlflow.db
+      --serve-artifacts
+      --artifacts-destination file:///mlflow-data/mlruns/mlartifacts
+      --host 0.0.0.0
+      --allowed-hosts "mlflow-server:5000,localhost:*"
+      --port 5000
     volumes:
-      - mlflow-data:/mlflow-data
+      - mlflow-data:/mlflow-data:rw
     ports:
-      - "5000:5000"
+      - "5000"
     depends_on:
       mlflow-init:
         condition: service_completed_successfully
+    networks:
+      - mlflow-network
 
 volumes:
   mlflow-data:
     name: mlflow-data
+
+networks:
+  # Use a deterministic network name so we can attach Tesseract
+  # containers to it more easily
+  mlflow-network:
+    name: tesseract-mlflow-server
diff --git a/inject_runtime_pyproject.py b/inject_runtime_pyproject.py
index 500a7eac..443deab5 100644
--- a/inject_runtime_pyproject.py
+++ b/inject_runtime_pyproject.py
@@ -28,7 +28,6 @@
         "fastapi",
         "httpx",  # required by fastapi test client
         "jsf",
-        "mlflow",
         "numpy",
         "pre-commit",
         "pytest",
diff --git a/production.uv.lock b/production.uv.lock
index e8df34e4..a63e7c03 100644
--- a/production.uv.lock
+++ b/production.uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 1
 requires-python = ">=3.10, <3.14"
 resolution-markers = [
     "python_full_version >= '3.12'",
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+sdist = { url = "https://files.pythonhosted.org/packages/bc/c1/bbac6a50d02774f91572938964c582fff4270eee73ab822a4aeea4d8b11b/accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872", size = 1377899 }
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+    { url = "https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7", size = 1395903 },
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+    { url = "https://files.pythonhosted.org/packages/b7/58/3bf0b7d474607dc7fd67dd1365c4e0f392c8177eaf4054e5ddee3ebd53b5/aiobotocore-2.26.0-py3-none-any.whl", hash = "sha256:a793db51c07930513b74ea7a95bd79aaa42f545bdb0f011779646eafa216abec", size = 87333 },
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+    { url = "https://files.pythonhosted.org/packages/73/ae/b48f95715333080afb75a4504487cbe142cae1268afc482d06692d605ae6/yarl-1.22.0-py3-none-any.whl", hash = "sha256:1380560bdba02b6b6c90de54133c81c9f2a453dee9912fe58c1dcced1edb7cff", size = 46814 },
 ]
 
 [[package]]
 name = "zipp"
 version = "3.23.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/e3/02/0f2892c661036d50ede074e376733dca2ae7c6eb617489437771209d4180/zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166", size = 25547, upload-time = "2025-06-08T17:06:39.4Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e3/02/0f2892c661036d50ede074e376733dca2ae7c6eb617489437771209d4180/zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166", size = 25547 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276, upload-time = "2025-06-08T17:06:38.034Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276 },
 ]
diff --git a/tesseract_core/runtime/meta/pyproject.toml b/tesseract_core/runtime/meta/pyproject.toml
index 5568c4af..7c2cee07 100644
--- a/tesseract_core/runtime/meta/pyproject.toml
+++ b/tesseract_core/runtime/meta/pyproject.toml
@@ -16,6 +16,7 @@ dependencies = [
     "pybase64<=1.4.3,>=1.4",
     "numpy<=2.3.5,>=1.26",
     "debugpy<=1.8.18,>=1.8.14",
+    "mlflow-skinny>=3.7.0",
 ]
 
 [project.scripts]
diff --git a/tesseract_core/runtime/mpa.py b/tesseract_core/runtime/mpa.py
index ac6003cb..c398a37f 100644
--- a/tesseract_core/runtime/mpa.py
+++ b/tesseract_core/runtime/mpa.py
@@ -16,8 +16,9 @@
 from io import UnsupportedOperation
 from pathlib import Path
 from typing import Any
-from urllib.parse import quote, urlparse
+from urllib.parse import ParseResult, quote, urlparse, urlunparse
 
+import mlflow
 import requests
 
 from tesseract_core.runtime.config import get_config
@@ -129,21 +130,10 @@ class MLflowBackend(BaseBackend):
 
     def __init__(self, base_dir: str | None = None) -> None:
         super().__init__(base_dir)
-        os.environ["GIT_PYTHON_REFRESH"] = (
-            "quiet"  # Suppress potential MLflow git warnings
-        )
-
-        try:
-            import mlflow
-        except ImportError as exc:
-            raise ImportError(
-                "MLflow is required for MLflowBackend but is not installed"
-            ) from exc
 
-        self.mlflow = mlflow
         tracking_uri = MLflowBackend._build_tracking_uri()
-        self._ensure_mlflow_reachable(tracking_uri)
         mlflow.set_tracking_uri(tracking_uri)
+        self._ensure_mlflow_reachable(tracking_uri)
 
     @staticmethod
     def _build_tracking_uri() -> str:
@@ -151,76 +141,69 @@ def _build_tracking_uri() -> str:
         config = get_config()
         tracking_uri = config.mlflow_tracking_uri
 
-        if not tracking_uri.startswith(("http://", "https://")):
-            # If it's a db file URI, convert to local path
-            tracking_uri = tracking_uri.replace("sqlite:///", "")
-
-            # Relative paths are resolved against the base output path
-            if not Path(tracking_uri).is_absolute():
-                tracking_uri = (Path(get_config().output_path) / tracking_uri).resolve()
-
-            tracking_uri = f"sqlite:///{tracking_uri}"
-        else:
-            username = config.mlflow_tracking_username
-            password = config.mlflow_tracking_password
-
-            if bool(username) != bool(password):
-                raise RuntimeError(
-                    "If one of TESSERACT_MLFLOW_TRACKING_USERNAME and TESSERACT_MLFLOW_TRACKING_PASSWORD is defined, "
-                    "both must be defined."
-                )
-
-            if username and password:
-                parsed = urlparse(tracking_uri)
-                # Reconstruct URI with embedded credentials
-                tracking_uri = (
-                    f"{parsed.scheme}://{quote(username)}:{quote(password)}@"
-                    f"{parsed.netloc}{parsed.path}"
-                )
-                if parsed.query:
-                    tracking_uri += f"?{parsed.query}"
-                if parsed.fragment:
-                    tracking_uri += f"#{parsed.fragment}"
-
-        return tracking_uri
+        parsed = urlparse(tracking_uri)
+        if not parsed.scheme:
+            tracking_uri = f"https://{tracking_uri}"
+
+        parsed = urlparse(tracking_uri)
+        if parsed.scheme not in ("http", "https"):
+            raise ValueError(
+                f"MLflow logging only supports accessing MLflow via HTTP/HTTPS (got URI scheme: {parsed.scheme})"
+            )
+
+        username = config.mlflow_tracking_username
+        password = config.mlflow_tracking_password
+
+        if bool(username) != bool(password):
+            raise RuntimeError(
+                "If one of TESSERACT_MLFLOW_TRACKING_USERNAME and TESSERACT_MLFLOW_TRACKING_PASSWORD is defined, "
+                "both must be defined."
+            )
+
+        if username and password:
+            # Reconstruct URI with embedded credentials
+            urlparts = parsed._asdict()
+            urlparts["netloc"] = f"{quote(username)}:{quote(password)}@{parsed.netloc}"
+            parsed = ParseResult(**urlparts)
+
+        return urlunparse(parsed)
 
     def _ensure_mlflow_reachable(self, tracking_uri: str) -> None:
         """Check if the MLflow tracking server is reachable."""
-        if tracking_uri.startswith(("http://", "https://")):
-            try:
-                response = requests.get(tracking_uri, timeout=5)
-                response.raise_for_status()
-            except requests.RequestException as e:
-                # Don't expose credentials in error message - use the original URI
-                config = get_config()
-                display_uri = config.mlflow_tracking_uri
-                raise RuntimeError(
-                    f"Failed to connect to MLflow tracking server at {display_uri}. "
-                    "Please make sure an MLflow server is running and TESSERACT_MLFLOW_TRACKING_URI is set correctly, "
-                    "or switch to file-based logging by setting TESSERACT_MLFLOW_TRACKING_URI to an empty string."
-                    "If your MLflow server has authentication enabled, please make sure that"
-                    "TESSERACT_MLFLOW_TRACKING_USERNAME and TESSERACT_MLFLOW_TRACKING_PASSWORD are set correctly."
-                ) from e
+        try:
+            response = requests.get(tracking_uri, timeout=5)
+            response.raise_for_status()
+        except requests.RequestException as e:
+            # Don't expose credentials in error message - use the original URI
+            config = get_config()
+            display_uri = config.mlflow_tracking_uri
+            raise RuntimeError(
+                f"Failed to connect to MLflow tracking server at {display_uri}. "
+                "Please make sure an MLflow server is running and TESSERACT_MLFLOW_TRACKING_URI is set correctly, "
+                "or switch to file-based logging by setting TESSERACT_MLFLOW_TRACKING_URI to an empty string."
+                "If your MLflow server has authentication enabled, please make sure that"
+                "TESSERACT_MLFLOW_TRACKING_USERNAME and TESSERACT_MLFLOW_TRACKING_PASSWORD are set correctly."
+            ) from e
 
     def log_parameter(self, key: str, value: Any) -> None:
         """Log a parameter to MLflow."""
-        self.mlflow.log_param(key, value)
+        mlflow.log_param(key, value)
 
     def log_metric(self, key: str, value: float, step: int | None = None) -> None:
         """Log a metric to MLflow."""
-        self.mlflow.log_metric(key, value, step=step)
+        mlflow.log_metric(key, value, step=step)
 
     def log_artifact(self, local_path: str) -> None:
         """Log an artifact to MLflow."""
-        self.mlflow.log_artifact(local_path)
+        mlflow.log_artifact(local_path)
 
     def start_run(self) -> None:
         """Start a new MLflow run."""
-        self.mlflow.start_run()
+        mlflow.start_run()
 
     def end_run(self) -> None:
         """End the current MLflow run."""
-        self.mlflow.end_run()
+        mlflow.end_run()
 
 
 def _create_backend(base_dir: str | None) -> BaseBackend:
diff --git a/tests/conftest.py b/tests/conftest.py
index b4c68769..d3818c14 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -6,6 +6,7 @@
 import random
 import string
 import subprocess
+import time
 from pathlib import Path
 from shutil import copytree
 from textwrap import indent
@@ -13,6 +14,7 @@
 from typing import Any
 
 import pytest
+import requests
 
 # NOTE: Do NOT import tesseract_core here, as it will cause typeguard to fail
 
@@ -491,3 +493,104 @@ def hacked_get(url, *args, **kwargs):
     monkeypatch.setattr(engine.requests, "get", hacked_get)
 
     yield mock_instance
+
+
+@pytest.fixture(scope="module")
+def mlflow_server():
+    """MLflow server to use in tests."""
+    # Check if docker-compose is available
+    try:
+        result = subprocess.run(
+            ["docker", "compose", "version"],
+            capture_output=True,
+            check=True,
+        )
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        pytest.fail("docker-compose not available")
+
+    # Start MLflow server with unique project name
+    project_name = f"test_mlflow_{int(time.time())}"
+
+    compose_file = (
+        Path(__file__).parent.parent / "extra" / "mlflow" / "docker-compose-mlflow.yml"
+    )
+
+    try:
+        # Start the services
+        subprocess.run(
+            [
+                "docker",
+                "compose",
+                "-f",
+                str(compose_file),
+                "-p",
+                project_name,
+                "up",
+                "-d",
+            ],
+            check=True,
+            capture_output=True,
+        )
+
+        res = subprocess.run(
+            [
+                "docker",
+                "compose",
+                "-f",
+                str(compose_file),
+                "-p",
+                project_name,
+                "ps",
+                "--format",
+                "json",
+            ],
+            check=True,
+            capture_output=True,
+            text=True,
+        )
+        service_data = json.loads(res.stdout)
+        service_port = service_data["Publishers"][0]["PublishedPort"]
+
+        # Note: We don't track containers/volumes here because docker-compose down -v
+        # will handle cleanup automatically in the finally block
+
+        # Wait for MLflow to be ready (with timeout)
+        tracking_uri = f"http://localhost:{service_port}"
+        max_wait = 30  # seconds
+        start_time = time.time()
+
+        while time.time() - start_time < max_wait:
+            try:
+                response = requests.get(tracking_uri, timeout=2)
+                if response.status_code == 200:
+                    break
+            except requests.RequestException:
+                pass
+            time.sleep(1)
+        else:
+            pytest.fail(f"MLflow server did not become ready within {max_wait}s")
+
+        yield tracking_uri
+
+    finally:
+        # Get logs for debugging
+        result = subprocess.run(
+            ["docker", "compose", "-f", str(compose_file), "-p", project_name, "logs"],
+            capture_output=True,
+            text=True,
+        )
+        print(result.stdout)
+        # Stop and remove containers
+        subprocess.run(
+            [
+                "docker",
+                "compose",
+                "-f",
+                str(compose_file),
+                "-p",
+                project_name,
+                "down",
+                "-v",
+            ],
+            capture_output=True,
+        )
diff --git a/tests/endtoend_tests/common.py b/tests/endtoend_tests/common.py
index b189a93b..ef7b1eaf 100644
--- a/tests/endtoend_tests/common.py
+++ b/tests/endtoend_tests/common.py
@@ -89,7 +89,9 @@ def build_tesseract(
     print_debug_info(result)
     assert result.exit_code == 0, result.exception
 
-    image_tag = json.loads(result.stdout.strip())[0]
+    # Parse the last line of stdout which contains the JSON array of image tags
+    stdout_lines = result.stdout.strip().split("\n")
+    image_tag = json.loads(stdout_lines[-1])[0]
 
     # This raise an error if the image does not exist
     client.images.get(image_tag)
diff --git a/tests/endtoend_tests/test_endtoend.py b/tests/endtoend_tests/test_endtoend.py
index a0afa118..77efbd77 100644
--- a/tests/endtoend_tests/test_endtoend.py
+++ b/tests/endtoend_tests/test_endtoend.py
@@ -6,12 +6,12 @@
 import json
 import os
 import shutil
-import sqlite3
 import subprocess
 import uuid
 from pathlib import Path
 from textwrap import dedent
 
+import mlflow
 import numpy as np
 import pytest
 import requests
@@ -1089,26 +1089,27 @@ def test_mpa_file_backend(tmpdir, mpa_test_image):
         assert artifact_data == "Test artifact content"
 
 
-@pytest.mark.parametrize("user", [None, "root", "12579:12579"])
-def test_mpa_mlflow_backend(mpa_test_image, tmpdir, user):
-    """Test the MPA (Metrics, Parameters, and Artifacts) submodule with MLflow backend."""
-    if user not in (None, "root"):
-        Path(tmpdir).chmod(0o777)
+def test_mpa_mlflow_backend(mlflow_server, mpa_test_image):
+    """Test the MPA (Metrics, Parameters, and Artifacts) submodule with MLflow backend, using a local MLflow server."""
+    # Hardcode some values specific to docker-compose config in extra/mlflow/mlflow-docker-compose.yaml
 
-    # Point MLflow to a local directory
+    # Inside containers, tracking URIs look like http://{service_name}:{internal_port}
+    mlflow_server_local = "http://mlflow-server:5000"
+    # Network name as specified in MLflow docker compose config
+    network_name = "tesseract-mlflow-server"
+
+    # Run the Tesseract, logging to running MLflow server
     run_cmd = [
         "tesseract",
         "run",
+        "--network",
+        network_name,
         "--env",
-        "TESSERACT_MLFLOW_TRACKING_URI=mlflow.db",
-        *(["--user", user] if user else []),
+        f"TESSERACT_MLFLOW_TRACKING_URI={mlflow_server_local}",
         mpa_test_image,
         "apply",
         '{"inputs": {}}',
-        "--output-path",
-        tmpdir,
     ]
-
     run_res = subprocess.run(
         run_cmd,
         capture_output=True,
@@ -1116,36 +1117,46 @@ def test_mpa_mlflow_backend(mpa_test_image, tmpdir, user):
     )
     assert run_res.returncode == 0, run_res.stderr
 
-    # Check for MLflow database file
-    mlflow_db_path = Path(tmpdir) / "mlflow.db"
-    assert mlflow_db_path.exists(), "Expected MLflow database file to exist"
+    # Use MLflow client to verify content was logged
+    mlflow.set_tracking_uri(mlflow_server)
 
-    # Query the database to verify content was logged
-    with sqlite3.connect(str(mlflow_db_path)) as conn:
-        cursor = conn.cursor()
+    # Get the most recent run (the one we just created)
+    from mlflow.tracking import MlflowClient
 
-        # Check parameters were logged
-        cursor.execute("SELECT key, value FROM params")
-        params = dict(cursor.fetchall())
-        assert params["test_parameter"] == "test_param"
-        assert params["steps_config"] == "5"  # MLflow stores params as strings
+    client = MlflowClient()
 
-        # Check metrics were logged
-        cursor.execute("SELECT key, value, step FROM metrics ORDER BY step")
-        metrics = cursor.fetchall()
-        assert len(metrics) == 5
+    # Get the default experiment (experiment_id="0")
+    experiment = client.get_experiment("0")
+    assert experiment is not None, "Default experiment not found"
 
-        # Verify some of the squared_step values
-        squared_metrics = [m for m in metrics if m[0] == "squared_step"]
-        assert len(squared_metrics) == 5
-        assert squared_metrics[0] == ("squared_step", 0.0, 0)
-        assert squared_metrics[1] == ("squared_step", 1.0, 1)
-        assert squared_metrics[4] == ("squared_step", 16.0, 4)
-
-        # Check artifacts were logged (MLflow stores artifact info in runs table)
-        cursor.execute("SELECT artifact_uri FROM runs")
-        artifact_uris = [row[0] for row in cursor.fetchall()]
-        assert len(artifact_uris) > 0  # At least one run with artifacts
+    runs = client.search_runs(experiment_ids=[experiment.experiment_id])
+    assert len(runs) > 0, "No runs found in MLflow"
+
+    # Get the most recent run
+    print(runs)
+    run = runs[0]
+    run_id = run.info.run_id
+
+    # Check parameters were logged
+    params = run.data.params
+    assert params["test_parameter"] == "test_param"
+    assert params["steps_config"] == "5"  # MLflow stores params as strings
+
+    # Check metrics were logged
+    metrics_history = client.get_metric_history(run_id, "squared_step")
+    assert len(metrics_history) == 5
+
+    # Verify some of the squared_step values
+    assert metrics_history[0].value == 0.0
+    assert metrics_history[0].step == 0
+    assert metrics_history[1].value == 1.0
+    assert metrics_history[1].step == 1
+    assert metrics_history[4].value == 16.0
+    assert metrics_history[4].step == 4
+
+    # Check artifacts were logged
+    artifacts = client.list_artifacts(run_id)
+    assert len(artifacts) > 0, "Expected at least one artifact to be logged"
 
 
 def test_multi_helloworld_endtoend(
diff --git a/tests/runtime_tests/test_mpa.py b/tests/runtime_tests/test_mpa.py
index 86cbd5f1..fe97ceb9 100644
--- a/tests/runtime_tests/test_mpa.py
+++ b/tests/runtime_tests/test_mpa.py
@@ -1,12 +1,12 @@
 # Copyright 2025 Pasteur Labs. All Rights Reserved.
 # SPDX-License-Identifier: Apache-2.0
 
-"""Tests for the MPA library."""
+"""Tests for the MPA module."""
 
 import csv
 import json
-import os
-import sqlite3
+import threading
+from http.server import BaseHTTPRequestHandler, HTTPServer
 
 import pytest
 
@@ -20,6 +20,45 @@
 )
 
 
+class Always400Handler(BaseHTTPRequestHandler):
+    """HTTP request handler that always returns 400."""
+
+    def do_GET(self):
+        """Handle GET requests with 400."""
+        self.send_response(400)
+        self.send_header("Content-Type", "text/plain")
+        self.end_headers()
+        self.wfile.write(b"Bad Request")
+
+    def do_POST(self):
+        """Handle POST requests with 400."""
+        self.send_response(400)
+        self.send_header("Content-Type", "text/plain")
+        self.end_headers()
+        self.wfile.write(b"Bad Request")
+
+    def log_message(self, format, *args):
+        """Suppress log messages."""
+        pass
+
+
+@pytest.fixture(scope="module")
+def dummy_mlflow_server():
+    """Start a dummy HTTP server that always returns 400."""
+    server = HTTPServer(("localhost", 0), Always400Handler)
+    port = server.server_address[1]
+
+    # Start server in a background thread
+    server_thread = threading.Thread(target=server.serve_forever, daemon=True)
+    server_thread.start()
+
+    try:
+        yield f"http://localhost:{port}"
+    finally:
+        # Shutdown server
+        server.shutdown()
+
+
 def test_start_run_context_manager():
     """Test that start_run works as a context manager."""
     with start_run():
@@ -140,124 +179,68 @@ def test_log_artifact_missing_file():
         backend.log_artifact("non_existent_file.txt")
 
 
-def test_mlflow_backend_creation(tmpdir):
-    """Test that MLflowBackend is created when mlflow_tracking_uri is set."""
-    pytest.importorskip("mlflow")  # Skip if MLflow is not installed
-    mlflow_db_file = tmpdir / "mlflow.db"
-    update_config(mlflow_tracking_uri=f"sqlite:///{mlflow_db_file}")
-    backend = mpa._create_backend(None)
-    assert isinstance(backend, mpa.MLflowBackend)
-
+def test_mlflow_backend_creation_fails_with_unreachable_server(dummy_mlflow_server):
+    """Test that MLflowBackend creation fails when server returns 400."""
+    update_config(mlflow_tracking_uri=dummy_mlflow_server)
+    with pytest.raises(
+        RuntimeError, match="Failed to connect to MLflow tracking server"
+    ):
+        mpa._create_backend(None)
 
-def test_mlflow_log_calls(tmpdir):
-    """Test MLflow backend logging functions with temporary directory."""
-    pytest.importorskip("mlflow")  # Skip if MLflow is not installed
-    mlflow_db_file = tmpdir / "mlflow.db"
-    update_config(mlflow_tracking_uri=f"sqlite:///{mlflow_db_file}")
 
-    with start_run():
-        log_parameter("model_type", "neural_network")
-        log_parameter("epochs", 100)
-
-        log_metric("accuracy", 0.85)
-        log_metric("loss", 0.25, step=1)
-
-        artifact_file = tmpdir / "model_config.json"
-        artifact_file.write_text("Test content", encoding="utf-8")
-        log_artifact(str(artifact_file))
-
-    # Verify MLflow database file was created
-    assert mlflow_db_file.exists()
-
-    # Query the database to verify content was logged
-    with sqlite3.connect(str(mlflow_db_file)) as conn:
-        cursor = conn.cursor()
-
-        # Check parameters were logged
-        cursor.execute("SELECT key, value FROM params")
-        params = dict(cursor.fetchall())
-        assert params["model_type"] == "neural_network"
-        assert params["epochs"] == "100"
-
-        # Check metrics were logged
-        cursor.execute("SELECT key, value, step FROM metrics ORDER BY step")
-        metrics = cursor.fetchall()
-        assert len(metrics) == 2
-        assert metrics[0] == ("accuracy", 0.85, 0)  # step defaults to 0
-        assert metrics[1] == ("loss", 0.25, 1)
-
-        # Check artifacts were logged (MLflow stores artifact info in runs table)
-        cursor.execute("SELECT artifact_uri FROM runs")
-        artifact_uris = [row[0] for row in cursor.fetchall()]
-        assert len(artifact_uris) > 0  # At least one run with artifacts
-
-        # Verify the artifact file was actually copied to the artifact location
-        artifact_found = False
-        for artifact_uri in artifact_uris:
-            if artifact_uri and os.path.exists(artifact_uri):
-                try:
-                    artifact_files = os.listdir(artifact_uri)
-                    if "model_config.json" in artifact_files:
-                        artifact_found = True
-                        break
-                except OSError:
-                    continue
-
-        assert artifact_found
-
-
-def test_build_tracking_uri_with_credentials():
-    pytest.importorskip("mlflow")
+def test_build_tracking_uri_with_credentials(dummy_mlflow_server):
     update_config(
-        mlflow_tracking_uri="http://localhost:5000",
+        mlflow_tracking_uri=dummy_mlflow_server,
         mlflow_tracking_username="testuser",
         mlflow_tracking_password="testpass",
     )
     tracking_uri = mpa.MLflowBackend._build_tracking_uri()
-    assert tracking_uri == "http://testuser:testpass@localhost:5000"
+    # Extract host:port from dummy_mlflow_server
+    expected_uri = dummy_mlflow_server.replace("http://", "http://testuser:testpass@")
+    assert tracking_uri == expected_uri
 
 
-def test_build_tracking_uri_without_credentials():
-    pytest.importorskip("mlflow")
+def test_build_tracking_uri_without_credentials(dummy_mlflow_server):
     update_config(
-        mlflow_tracking_uri="http://localhost:5000",
+        mlflow_tracking_uri=dummy_mlflow_server,
         mlflow_tracking_username="",
         mlflow_tracking_password="",
     )
     tracking_uri = mpa.MLflowBackend._build_tracking_uri()
-    assert tracking_uri == "http://localhost:5000"
+    assert tracking_uri == dummy_mlflow_server
 
 
-def test_build_tracking_uri_url_encoded_credentials():
-    pytest.importorskip("mlflow")
+def test_build_tracking_uri_url_encoded_credentials(dummy_mlflow_server):
+    # Use a dummy HTTPS URL for testing URL encoding
+    dummy_https_url = dummy_mlflow_server.replace("http://", "https://")
     update_config(
-        mlflow_tracking_uri="https://mlflow.example.com",
+        mlflow_tracking_uri=dummy_https_url,
         mlflow_tracking_username="user@example.com",
         mlflow_tracking_password="p@ss:w0rd!",
     )
     tracking_uri = mpa.MLflowBackend._build_tracking_uri()
-    assert (
-        tracking_uri == "https://user%40example.com:p%40ss%3Aw0rd%21@mlflow.example.com"
-    )
+    # Verify that special characters are URL-encoded
+    assert "user%40example.com" in tracking_uri
+    assert "p%40ss%3Aw0rd%21" in tracking_uri
 
 
-def test_build_tracking_uri_with_path_and_query():
-    pytest.importorskip("mlflow")
+def test_build_tracking_uri_with_path_and_query(dummy_mlflow_server):
+    # Add path and query to dummy server URL
+    uri_with_path = f"{dummy_mlflow_server}/api/mlflow?param=value"
     update_config(
-        mlflow_tracking_uri="http://localhost:5000/api/mlflow?param=value",
+        mlflow_tracking_uri=uri_with_path,
         mlflow_tracking_username="testuser",
         mlflow_tracking_password="testpass",
     )
     tracking_uri = mpa.MLflowBackend._build_tracking_uri()
-    assert (
-        tracking_uri == "http://testuser:testpass@localhost:5000/api/mlflow?param=value"
-    )
+    # Verify credentials are inserted correctly with path and query preserved
+    assert "testuser:testpass@" in tracking_uri
+    assert "/api/mlflow?param=value" in tracking_uri
 
 
-def test_build_tracking_uri_username_without_password():
-    pytest.importorskip("mlflow")
+def test_build_tracking_uri_username_without_password(dummy_mlflow_server):
     update_config(
-        mlflow_tracking_uri="http://localhost:5000",
+        mlflow_tracking_uri=dummy_mlflow_server,
         mlflow_tracking_username="testuser",
         mlflow_tracking_password="",
     )
@@ -268,10 +251,9 @@ def test_build_tracking_uri_username_without_password():
         mpa.MLflowBackend._build_tracking_uri()
 
 
-def test_build_tracking_uri_password_without_username():
-    pytest.importorskip("mlflow")
+def test_build_tracking_uri_password_without_username(dummy_mlflow_server):
     update_config(
-        mlflow_tracking_uri="http://localhost:5000",
+        mlflow_tracking_uri=dummy_mlflow_server,
         mlflow_tracking_username="",
         mlflow_tracking_password="testpass",
     )
@@ -282,14 +264,14 @@ def test_build_tracking_uri_password_without_username():
         mpa.MLflowBackend._build_tracking_uri()
 
 
-def test_build_tracking_uri_sqlite_ignores_credentials():
-    pytest.importorskip("mlflow")
+def test_build_tracking_uri_non_http_scheme_raises_error():
+    """Test that non-HTTP/HTTPS schemes raise an error."""
     update_config(
         mlflow_tracking_uri="sqlite:///mlflow.db",
-        mlflow_tracking_username="testuser",
-        mlflow_tracking_password="testpass",
+        mlflow_tracking_username="",
+        mlflow_tracking_password="",
     )
-    tracking_uri = mpa.MLflowBackend._build_tracking_uri()
-    assert "testuser" not in tracking_uri
-    assert "testpass" not in tracking_uri
-    assert tracking_uri.startswith("sqlite:///")
+    with pytest.raises(
+        ValueError, match="MLflow logging only supports accessing MLflow via HTTP/HTTPS"
+    ):
+        mpa.MLflowBackend._build_tracking_uri()