diff --git a/sagemaker-core/pyproject.toml b/sagemaker-core/pyproject.toml
index 7129e9fe98..accbfc9816 100644
--- a/sagemaker-core/pyproject.toml
+++ b/sagemaker-core/pyproject.toml
@@ -12,7 +12,7 @@ authors = [
 readme = "README.rst" 
 dependencies = [
   # Add your dependencies here (Include lower and upper bounds as applicable)
-    "boto3>=1.42.2,<2.0.0",
+    "boto3>=1.43.20,<2.0.0",
     "pydantic>=2.0.0,<3.0.0",
     "PyYAML>=6.0, <7.0",
     "jsonschema<5.0.0",
diff --git a/sagemaker-core/src/sagemaker/core/utils/utils.py b/sagemaker-core/src/sagemaker/core/utils/utils.py
index 5ac288eab7..9f916902f4 100644
--- a/sagemaker-core/src/sagemaker/core/utils/utils.py
+++ b/sagemaker-core/src/sagemaker/core/utils/utils.py
@@ -340,7 +340,7 @@ class SageMakerClient(metaclass=SingletonMeta):
 
     @classmethod
     def reset(cls):
-        """Reset the singleton instance so the next call re-reads env vars."""
+        """Reset the singleton instance."""
         SingletonMeta._instances.pop(cls, None)
 
     def __init__(
@@ -368,53 +368,12 @@ def __init__(
         self.config = Config(user_agent_extra=get_user_agent_extra_suffix())
         self.session = session
         self.region_name = region_name
-        # Read region from environment variable, default to us-west-2
-        import os
-        env_region = os.environ.get('SAGEMAKER_REGION', region_name)
-        env_stage = os.environ.get('SAGEMAKER_STAGE', 'prod')  # default to gamma
-        logger.info(f"Runs on sagemaker {env_stage}, region:{env_region}")
-
-        endpoint_url = os.environ.get('SAGEMAKER_ENDPOINT')
-        runtime_endpoint_url = os.environ.get('SAGEMAKER_RUNTIME_ENDPOINT')
-
-        # TODO: Remove post-launch. This loads a custom botocore service model
-        # (from the 'sample/' directory) that includes pre-GA Job APIs
-        # (CreateJob, DescribeJob, ListJobs, etc.) not yet in the public
-        # botocore release. Once these APIs ship in the official botocore
-        # service-2.json, this custom loader is unnecessary and the standard
-        # session.client("sagemaker", endpoint_url=...) path will work.
-        #
-        # NOTE: The custom data loader is registered on the caller-provided
-        # session's underlying botocore session so that the "sagemaker"
-        # control-plane client keeps the caller's credentials/profile. Using a
-        # fresh botocore.session.get_session() here would silently fall back to
-        # the default AWS profile and break cross-account calls (issue #6069).
-        import botocore.loaders
-        import pathlib
-
-        # Look for bundled service model inside the package first,
-        # fall back to source-tree layout (sample/ as sibling of src/)
-        _bundled = pathlib.Path(__file__).resolve().parent.parent / "data" / "sample"
-        _source_tree = pathlib.Path(__file__).resolve().parent.parent.parent.parent.parent / "sample"
-        sample_model_dir = str(_bundled if _bundled.exists() else _source_tree)
-        loader = botocore.loaders.Loader(
-            extra_search_paths=[sample_model_dir],
-            include_default_search_paths=True,
-        )
-        # boto3.Session exposes its underlying botocore session as `_session`.
-        session._session.register_component('data_loader', loader)
 
         self.sagemaker_client = session.client(
-            "sagemaker",
-            region_name=env_region,
-            endpoint_url=endpoint_url,
-            config=self.config,
+            "sagemaker", region_name, config=self.config
         )
-
         self.sagemaker_runtime_client = session.client(
-            "sagemaker-runtime", region_name,
-            endpoint_url=runtime_endpoint_url,
-            config=self.config,
+            "sagemaker-runtime", region_name, config=self.config
         )
         self.sagemaker_featurestore_runtime_client = session.client(
             "sagemaker-featurestore-runtime", region_name, config=self.config