Additionally, I log the model as shown below, with MicrosoftResnet50Model being my custom inference class with load_context and predict methods:
with mlflow.start_run():
model_info = mlflow.pyfunc.log_model(
REGISTERED_MODEL_NAME,
python_model=MicrosoftResnet50Model(),
input_example=api_input_example,
artifacts={"model_path": MODEL_PATH},
pip_requirements=[
f"transformers=={transformers.__version__}",
"torch==2.0.1"
],
signature=signature,
registered_model_name=f"{CATALOG}.{SCHEMA}.{REGISTERED_MODEL_NAME}"
)

Hey  VELU1122,

did you find a solution for it. We are struggling with the same problem currently. 

Thanks 

Greetings @VELU1122 ,  you’re correct that the Databricks Model Serving container is isolated, so you can’t rely on cluster-only affordances like mounts or executor-distributed file utilities. The reliable way to read from Unity Catalog (UC) Volumes in a serving endpoint is to use the Databricks Files API / SDK with an endpoint-injected credential, and address files by their UC Volumes path, for example /Volumes/<catalog>/<schema>/<volume>/<relative_path>.

What works from Model Serving

• Use the Files REST API or the Databricks SDK (WorkspaceClient.files) to list, download, and upload files in UC Volumes with paths like /Volumes/<catalog>/<schema>/<volume>/.... This is supported for managing and reading files directly from Volumes, and avoids the need for dbutils or mounts inside the serving container.
• Inject credentials into the serving container using environment variables backed by Databricks secrets. Define DATABRICKS_HOST as plain text and DATABRICKS_TOKEN (or use OAuth for a service principal) as a secret in the endpoint config; then use the SDK to call the Files API at inference time.
• Ensure the endpoint’s identity (the user or service principal that created the endpoint) has UC privileges (for example, READ FILES on the Volume). Endpoint identity is fixed at creation and is used for UC access checks; if it lacks privileges, recreate the endpoint under an identity that has access.
• If your Volume is external, you can also access data via cloud URIs (s3://, abfss://, gs://) as part of Volumes GA, but you still must provide cloud credentials in the serving container (for example via an instance profile on the endpoint or provider-specific auth). For many scenarios, the Files API / SDK is simpler and keeps governance in UC.

What doesn’t work in Model Serving

• Avoid dbutils.fs.mount or relying on FUSE-style local paths in serving containers; use Files API / SDK instead. Model Serving doesn’t run notebook executors and doesn’t support the same dbutils semantics; Volumes are intended for path-based governance and programmatic access via APIs and POSIX-like paths, not runtime mounts in serving.

Recommended pattern 1. Configure environment variables with secrets on your endpoint:

• In Serving UI or via REST/SDK, add: * DATABRICKS_HOST: https://<your-workspace-url> (plain text). * DATABRICKS_TOKEN: {{secrets/<scope>/<key>}} (secret). * Alternatively, use OAuth M2M for a service principal and inject DATABRICKS_CLIENT_ID / DATABRICKS_CLIENT_SECRET and fetch short-lived tokens at runtime, then call the Files API. This avoids PATs and is recommended for unattended endpoints.

2. From your custom python_model class, read files with the SDK: ```python import os import io from databricks.sdk import WorkspaceClient

def _read_volume_file(self, path: str) -> bytes:
    # path like "/Volumes/<catalog>/<schema>/<volume>/images/cat.jpg"
    resp = self.w.files.download(path)  # returns a response with .contents (bytes)
    return resp.contents

3. Pass any constant namespace values or paths you need as artifacts/params when logging the model or as endpoint environment variables, so your class can construct the /Volumes/... path at runtime.
4. If you truly need direct cloud access (for external Volumes), configure the endpoint with an instance profile or provider credentials and use the cloud SDK/URI. Otherwise, prefer the Files API route for simplicity and governance consistency.

Why the errors occur

• “No such file or directory” happens when using local filesystem paths that aren’t available in the serving container; UC Volume access in Serving should go through the Files API/SDK and Volume paths, not mounts.
• dbutils is notebook/cluster-bound; Model Serving supports environment variables and secrets injection for external access, not dbutils mounts. Use the Files API / SDK instead of dbutils in serving.

Alternative strategies

• If the files are static assets required for inference (labels, templates, small configs), bundle them as MLflow model artifacts at log time and access them via context.artifacts rather than reaching out to Volumes during inference. This reduces I/O and removes external dependencies at serving time.
• For high-throughput batch scenarios that require broad data scans, consider Jobs on UC-enabled compute reading from Volumes with Spark, and write outputs to tables; use Model Serving for low-latency point queries. Volumes are fully supported across Spark, SQL, dbutils, REST, CLI, and SDKs, so you can mix patterns as needed.

Endpoint config snippets

• Volumes GA capabilities and cloud URI access for external Volumes.
• Volumes object model, path rules, and limitations (must include the volume name in paths, intended for path-based access).
• Serving endpoint identity and UC access implications.