diff --git a/misc/py-sagemaker-train/Makefile b/misc/py-sagemaker-train/Makefile
index f457f72a9c19..e098881acf08 100644
--- a/misc/py-sagemaker-train/Makefile
+++ b/misc/py-sagemaker-train/Makefile
@@ -1,30 +1,29 @@
 PORTNAME=	sagemaker-train
 DISTVERSION=	1.0
 CATEGORIES=	misc python # machine-learning
 MASTER_SITES=	PYPI
 PKGNAMEPREFIX=	${PYTHON_PKGNAMEPREFIX}
 DISTNAME=	${PORTNAME:S/-/_/}-${PORTVERSION}
 
 MAINTAINER=	yuri@FreeBSD.org
-COMMENT=	SageMaker: Library for training & deploying models on Amazon SageMaker
-WWW=		https://sagemaker.readthedocs.io/en/stable/ \
-		https://github.com/aws/sagemaker-python-sdk
+COMMENT=	SageMaker: Amazon Training Toolkit
+WWW=		https://github.com/aws/sagemaker-training-toolkit
 
 LICENSE=	APACHE20
 LICENSE_FILE=	${WRKSRC}/LICENSE
 
 BUILD_DEPENDS=	${PY_SETUPTOOLS} \
 		${PYTHON_PKGNAMEPREFIX}wheel>0:devel/py-wheel@${PY_FLAVOR}
 RUN_DEPENDS=	${PYTHON_PKGNAMEPREFIX}graphene>=3:devel/py-graphene@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}paramiko>=2.11.0:security/py-paramiko@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}sagemaker-core>=2.0.0:misc/py-sagemaker-core@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}tblib>=1.7.0:devel/py-tblib@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}typing-extensions>=4.9.0:devel/py-typing-extensions@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}pyyaml>=6.0:devel/py-pyyaml@${PY_FLAVOR}
 
 USES=		python
 USE_PYTHON=	pep517 autoplist
 
 NO_ARCH=	yes
 
 .include <bsd.port.mk>
diff --git a/misc/py-sagemaker-train/pkg-descr b/misc/py-sagemaker-train/pkg-descr
index 16dad05472d1..00dd3e52a609 100644
--- a/misc/py-sagemaker-train/pkg-descr
+++ b/misc/py-sagemaker-train/pkg-descr
@@ -1,11 +1,6 @@
-sagemaker-train is a part of the SageMaker Python SDK.
+sagemaker-train is an Amazon SageMaker Training Toolkit.
 
-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.
+This library allows you to write a script to train a model in Amazon
+SageMaker. It provides functionality for your training script to communicate
+with the SageMaker training environment, including writing metrics, saving
+models, and accessing hyperparameters and other configuration.