diff --git a/misc/py-sagemaker-core/Makefile b/misc/py-sagemaker-core/Makefile
index 7ecf532ab0ec..bd9968f5909e 100644
--- a/misc/py-sagemaker-core/Makefile
+++ b/misc/py-sagemaker-core/Makefile
@@ -1,43 +1,44 @@
 PORTNAME=	sagemaker-core
 DISTVERSION=	2.0.1
 CATEGORIES=	misc python # machine-learning
 MASTER_SITES=	PYPI
 PKGNAMEPREFIX=	${PYTHON_PKGNAMEPREFIX}
 DISTNAME=	${PORTNAME:S/-/_/}-${PORTVERSION}
 
 MAINTAINER=	yuri@FreeBSD.org
-COMMENT=	Sagemaker core functionalities
-WWW=		https://github.com/aws/sagemaker-core
+COMMENT=	SageMaker: Core functionalities
+WWW=		https://github.com/aws/sagemaker-python-sdk \
+		https://github.com/aws/sagemaker-core
 
 LICENSE=	APACHE20
 LICENSE_FILE=	${WRKSRC}/LICENSE
 
 BUILD_DEPENDS=	${PY_SETUPTOOLS} \
 		${PYTHON_PKGNAMEPREFIX}wheel>0:devel/py-wheel@${PY_FLAVOR}
 RUN_DEPENDS=	${PYTHON_PKGNAMEPREFIX}attrs>=20.3.0:devel/py-attrs@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}boto3>=1.35.75<2.0.0:www/py-boto3@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}importlib-metadata>=7.0>=1.4.0:devel/py-importlib-metadata@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}jsonschema>0:devel/py-jsonschema@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}mock>0:devel/py-mock@${PY_FLAVOR} \
 		${PYNUMPY} \
 		${PYTHON_PKGNAMEPREFIX}omegaconf>=2.1.0:devel/py-omegaconf@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}packaging>=20.0:devel/py-packaging@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}pandas>=1.0.0:math/py-pandas@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}platformdirs>=4.0.0<5.0.0:devel/py-platformdirs@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}protobuf>0:devel/py-protobuf@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}pydantic2>=2.0.0<3.0.0:devel/py-pydantic2@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}pytorch>=1.9.0:misc/py-pytorch@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}pytz>=2021.1:devel/py-pytz@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}pyyaml>=6.0:devel/py-pyyaml@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}requests>=2.20.0<3.0.0:www/py-requests@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}rich>=13.0.0:textproc/py-rich@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}schema>=0.7.5:devel/py-schema@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}smdebug-rulesconfig>=1.0.1:misc/py-smdebug-rulesconfig@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}typing-extensions>=4.9.0:devel/py-typing-extensions@${PY_FLAVOR}
 
 USES=		python
 USE_PYTHON=	pep517 autoplist
 
 NO_ARCH=	yes
 
 .include <bsd.port.mk>
diff --git a/misc/py-sagemaker-core/pkg-descr b/misc/py-sagemaker-core/pkg-descr
index 3ecd7c4fbe95..dfd4e8a947b2 100644
--- a/misc/py-sagemaker-core/pkg-descr
+++ b/misc/py-sagemaker-core/pkg-descr
@@ -1,7 +1,11 @@
-sagemaker-core is a Python SDK designed to provide an object-oriented interface
-for interacting with Amazon SageMaker resources. It offers full parity with
-SageMaker APIs, allowing developers to leverage all SageMaker capabilities
-directly through the SDK. sagemaker-core introduces features such as dedicated
-resource classes, resource chaining, auto code completion, comprehensive
-documentation and type hints to enhance the developer experience as well as
-productivity.
+sagemaker-core is a part of the SageMaker Python SDK.
+
+SageMaker Python SDK is an open source library for training and deploying
+machine learning models on Amazon SageMaker.
+
+With the SDK, you can train and deploy models using popular deep learning
+frameworks Apache MXNet and TensorFlow. You can also train and deploy
+models with Amazon algorithms, which are scalable implementations of core
+machine learning algorithms that are optimized for SageMaker and GPU training.
+If you have your own algorithms built into SageMaker compatible Docker
+containers, you can train and host models using these as well.